Citations related to NETWORKS (works cited listed at bottom):

“Every surface is actually a screen that hides while showing and shows while hiding.” Taylor, Mark C. The Moment of Complexity: Emerging Network Culture. University of Chicago. 2001. p. 200.


“In network culture, subjects are screens and knowing is screening.” Taylor, Mark C. The Moment of Complexity: Emerging Network Culture. University of Chicago. 2001. p. 200.


“Fields are best described by their metaphors, methods, problem agendas, and competitors. The metaphors capture the gut intuitions; the methods describe the tools at hand; the problem agendas describe what the field does; and the competitors describe the outer boundaries.” Willard, Charles Arthur. Liberalism and the Problem of Knowledge: A New Rhetoric for Modern Democracy. University of Chicago Press. 1996. p. 190.


“I conclude that we are gradually discovering that we are, in effect, incarnations of worldwide webs and global networks whose complexity is fraught with danger as well as opportunity.” Taylor, Mark C. The Moment of Complexity: Emerging Network Culture. University of Chicago. 2001. p. 17.


“The bird a nest, the spider a web, man friendship.” Blake, William. Quoted in Moore, Thomas. Soul Mates: Honoring the Mysteries of Love and Relationship. Harper. 1994. P. 43.


“... two opposite and enduring reactions had emerged clearly. This was due to the profound ambivalence created among peoples outside, or on the fringes of, the metropolitan webs, who, recognizing their own vulnerability, either sought to remedy the situation by borrowing and adapting whatever made others so powerful–or, alternately, deliberately tried to reject outside corruption by defending, strengthening, and reaffirming what made them different.

“Consequently, as skills, goods, and attitudes spread from the heartland of each civilization, a cultural slope defined itself, and social and environmental strains multiplied everywhere along it.” McNeill, J.R. and William H., The Human Web: A Bird’s-Eye View of World History. W.W. Norton. 2003. P. 81.


“Close parallels in the development of the Americas, Eurasia, and Africa are apparent. Despite enormous cultural and geographic differences across the continents, it is tempting to suppose that these parallels arose because farming peoples needed the services first of priests and then of warriors to thrive....

“Priests eventually ceded primacy to warriors, for straightforward reasons. For when priestly management succeeded in creating substantial surpluses, organized robbery became a feasible way of life. This opened a niche for professional fighting men to protect communities from plunderers by monopolizing organized violence in return for negotiated protection payments.” McNeill, J.R. and William H., The Human Web: A Bird’s-Eye View of World History. W.W. Norton. 2003. Pps. 114-5.


Weiss, Paul (quoted by Terrence Deacon), "In trying to restore the loss of information suffered by thus lifting isolated fragments out of context, we have assigned the job of reintegration to a corps of anthropomorphic gremlins. As a result, we are now plagued--or blessed, depending on one's party view--with countless demigods, like those in antiquity, doing the jobs we do not understand: the organizers, operators, inductors, repressors, promoters, regulators, etc.--all prosthetic devices to make up for the amputations which we have allowed to be perpetrated on the organic wholeness, or to put it more innocuously, the 'systems' character, of nature and of our thinking about nature."


“If early in the evolution of life, metabolic networks grew by adding new metabolites, then the most highly connected metabolites should also be the phylogenetically oldest. Glycolysis and the tricarboxylic acid cycle are perhaps the most ancient metabolic pathways, and various of their intermediates occur near the top of our lists, along with the amino acids thought to be used earliest.” David Fell & Andreas Wagner. Nature Biotechnology. The small world of metabolism. Vol. 18. November 2000. Pp. 1121-2. Taken from Newman, Mark, Barabasi, & Watts. 2006. The Structure and Dynamics of Networks. Princeton University Press. Pp. 215-6.


“Jeong et al. also found that the average path length in their networks was small: most pairs of molecules can be linked by a path of only three reactions. This finding has biological significance. If one were to find that the shortest chemical path between two molecules was 100, then any change in the concentration of the first molecule would have to go through 100 intermediate reactions before reaching the second molecule. Such a long path would certainly put a strong damper on any correlations between the concentrations of the two metabolites, with perturbations decaying quickly as they moved along the path. By contrast, the short path lengths found by Jeong et al indicate that change in concentration of a molecule should reach others quickly.”

“While unexpectedly short, the precise value of the mean path length was not the most intriguing aspect of the results for path lengths. Jeong et al also looked at how path lengths vary between organisms. Since the networks of the different organisms studied vary considerably in size, one might expect the mean path length to vary also, as it does in other networks. Surprisingly, however, measurements indicated that there was very little variation in the path length between organisms. The tiny network of a parasitic bacterium has the same mean path length as the highly developed network of a large multicellular organism. The measurements indicated that most cells have the same hubs as well. For the vast majority of organisms, the ten most connected molecules are the same. ATP is almost always the biggest hub, followed closely by adenosine diphosphate (ADP) and water. The role of ATP, ADP, and water as prominent hubs is certainly not surprising. Their roles in the storage and release of the energy that powers most functions of the cell makes them crucial to almost every biochemical process.” Newman, Mark, Albert Barabasi, Duncan Watts. 2006. The Structure and Dynamics of Networks. Princeton University Press. Pp. 175-6. Citing Jeong, H., B. Tombor, R. Albert, Z. N. Oltval, A. Barabasi. 2000. Nature. “The large-scale organization of metabolic networks.” Vol. 407, October 5, 2000. Pp. 651-654.


“If early in the evolution of life, metabolic networks grew by adding new metabolites, then the most highly connected metabolites should also be the phylogenetically oldest. Glycolysis and the tricarboxylic acid cycle are perhaps the most ancient metabolic pathways, and various of their intermediates occur near the top of our lists, along with the amino acids thought to be used earliest.” David Fell & Andreas Wagner. Nature Biotechnology. The small world of metabolism. Vol. 18. November 2000. Pp. 1121-2. Taken from Newman, Mark, Albert Barabasi, Duncan Watts. 2006. The Structure and Dynamics of Networks. Princeton University Press. Pp. 215-6.


“On the other hand, numerous examples convincingly indicate that in real systems a node’s connectivity and growth rate does not depend on its age alone. For example, in social systems some individuals are better in turning a random meting into a lasting social link than others. On the www some documents through a combination of good content and marketing acquire a large number of links in a very short time, easily overtaking older websites. Finally, some research papers in a short time frame acquire a very large number of citations. We tend to associate these differences with some intrinsic quality of the nodes, such as the social skills of an individual, the content of a web page, or the content of a scientific article. We will call this the node’s fitness, describing its ability to compete for links at the expense of other nodes.” Bianconi, G. & A. Barabasi. “Competition and multiscaling in evolving networks.” Originally appearing in: Europhysics Letters. 54 (4), pp. 436-442 (2001). Cited in Newman, Mark, Albert Barabasi, Duncan Watts. 2006. The Structure and Dynamics of Networks. Princeton University Press. P. 362.


“... each type of biological network is built of a distinctive set of network motifs. Each of the network motifs can carry out defined dynamical functions. Developmental transcription networks display many of the motifs found in sensory transcription networks. They also have additional network motifs that correspond to the irreversible decisions and slower dynamics of developmental processes. In particular, two-node positive feedback loops, regulated by a third transcription factor, can provide lock-on to a cell fate or provide toggle switches between two different fates. Long cascades can orchestrate developmental programs that take place over multiple cell generations.”

“Signal transduction networks show faster dynamics and may utilize multi-layer perceptrons to perform computations on numerous input stimuli. Multi-layer perceptrons can allow even relatively simple units to perform detailed computations. Multi-layer perceptrons can perform more intricate computations than single-layer perceptrons. The dynamical features of these neworks are affected by feedback loops and additional interactions.” Alon, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. 2007. Chapman & Hall/CRC. P. 127.


“Transcription factors are usually designed to transit rapidly between active and inactive molecular states, at a rate that is modulated by a specfic environmental signal (input). Each active transcription factor can bind the DNA to regulate the rate at which specific target genes are read. The genes are read (transcribed) into mRNA, which is then translated into protein, which can act on the environment. The activities of the transcription factors in a cell therefore can be considered an internal representation of the environment.” Alon, Uri. 2007. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC. P. 7.


“Proteins are potentially regulated in every step of their synthesis process, including the following post-transcriptional regulation interactions: (1) rate of degradation of the mRNA, (2) rate of translation, controlled primarily by sequences in the mRNA that bind the ribosomes and by mRNA-binding regulatory proteins and regulatory RNA molecules and (3) rate of active and specific protein degradation. In eukaryotes, regulation also occurs on the level of mRNA splicing and transport in the cell.” Alon, Uri. 2007. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC. P. 18, footnote.


“It is important to note that most biological networks are sparse, which is to say that only a tiny fraction of the possible edges actually occur. Sparse networks are defined by p << 1. For example, in the Escherichia coli network we use as example, there are about 400 nodes and 500 edges, so that p ~0.002. One reason that biological networks are sparse is that each interaction in the network is selected by evolution against mutations that would rapidly abolish the interaction. Thus, only useful interactions are maintained.” Alon, Uri. 2007. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC. P. 43.


“In addition to transcription networks, the cell uses several other networks of interactions, such as protein-protein interaction networks, signal transduction networks, and metabolic networks. Each network corresponds to a different mode of interaction between boimolecules. Thus, one can think of the cell as made of several superimposed networks, with different colors of edges. Transcription networks correspond to one color of edges, protein-protein interactions to a different color, etc.”

“There is a separation of timescales between these different network layers. Transcription networks are among the slowest of these networks. As we have discussed, they often show dynamics on the scale of hours. Other networks in the cell function on much faster timescales. For example, signal transduction networks, which process information using interactions between signaling proteins, can function on the timescale of seconds to minutes.” Alon, Uri. 2007. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC. Pp. 97-8.


“The regulated feedback motif can be considered as a memory element: the regulator Z can switch the feedback loop from one state to another, such that the state persists even after Z is deactivated. Hence, the circuit can remember whether Z was active in the past.” Alon, Uri. 2007. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC. P. 100-1. (Z has directed interactions to each X and Y and X and Y have directed interactions between them in both directions that are either both positive or both negative. If X and Y are positively feedbacked, then Z has both positive edges to X and Y, but if X and Y have two inhibiting interactions between them, the Z has one positive directed edge and one negative edge.)


“... we note that the set of network motifs found in neuronal networks is not identical to those found in transcription networks. For example, although the FFL is a motif in both of these networks, the FFLs are joined together in neuronal networks in different ways than in transcription networks. This can be seen by considering the topological generalizations of the FFL pattern.”

“The most common FFL generalization in transcription networks is the multi-output FFL, as we saw in Chapter 5. In contrast, a distinct generalization, the multi-input FFL, is the most common generalization in the C. elegans neuronal network.” Alon, Uri. 2007. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC. P. 122.


“An additional intriguing set of questions concerns the interplay between the ecology and the biological design of the organism. We currently have more information on the detailed structure of biological circuits than on the environment in which they evolved. We know little about the constraints and functional goals of cells within complex organisms, and are only beginning to understand the optimizations and trade-offs that underlie their design. It is an interesting question whether it would be possible to form a theory of biological design that can help unify aspects of ecology, evolution, and molecular biology.” Alon, Uri. 2007. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC. Pp. 238-9.


“A link is defined as weak when its addition or removal does not change the mean value of a target measure in a statistically discernible way.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 3.


“How do networks start to behave as elements and assemble into top networks? Symbiosis-driven nestedness, also called integration by Sole et al., occurs if a network enjoys a longer period of relative stability and then, by associating with other networks, all become elements of a top network.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 34.


“A special feature of steady-state thermodynamics is housekeeping heat. This housekeeping heat is defined as the energy preventing non-equilibrium steady states from shifting to equilibrium. Self-organizing networks suffer various types of random damage. Therefore, if the network remained static, it would soon become dysfunctional. Some networks have developed highly specific screening systems which recognize and repair random damage. On the one hand, this process requires energy, which arrives in the form of perturbations or noise. On the other hand, noise-triggered network restructuring will repeat a few steps of the original self-organization and therefore constitutes a much cheaper way of providing a continuous repair function, with the additional advantage that it is always adaptive with respect to the actual environment of the network.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 55.


“The network considers as information only those perturbations which arrive often enough for it to develop a learned response, or which are important enough for the survival of the network. These perturbations get the network highways to go right round. All other perturbations are brought right round in local segments of a well-designed network, until they level off.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 58.


“Shenhav et al. (2005) described the idea that prebiotic catalytic networks might have a random configuration and raised the question as to how this early molecular ensemble changed to the scale-free metabolic networks we observe today. A topological phase transition may give a clue here. As the number of prebiotic networks grew and system resources became sparse due to mutual competition, the random network may have been forced to change configuration to the scale-free degree distribution we observe today. The differences in the reports on the degree distribution of metabolic networks may also be partially derived from the different configuration of these networks under various levels of cellular stress.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 79.


“Many of these networks, like the transcriptional or metabolic networks, are functionally defined. In the metabolic network, the various metabolites are the elements and the enzyme reactions are the links. In the gene transcription network, the transcription factors and genes are the elements and functional interactions between them are the connecting links. Other networks, like the protein-protein interaction network or the cytoskeletal network, have proteins as elements, and permanent or transient bonds between these proteins as links. All these networks overlap one another considerably, and some of them (like the membrane-organelle network) contain modules of other networks).” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 127


“Moreover, words display a scale-free distribution, the so-called Zipf law. In the Zipf analysis, all words are arranged in a rank order from the most frequent to the least frequent. Interestingly, the most frequent words have the least semantic content.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 219.


“Moreover, the internalization of the intentions, motivations, words and deeds of the 4 actors requires the audience to think to the fifth order, so to speak. A typical sentence to reflect this complexity is the following: I believe that A supposes that B intends to guess how C understands what D thinks. According to Dunbar’s measures, fifth-order thinking is the usual cognitive limit.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 227.


“You are built from networks, you make networks, and you live in networks. You should understand how they work and how you can influence them.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 273.


“Networks offer you both the generality of rules and the delicate details. You may remember the nested networks. If you keep your distance, they behave like a point. If you go close to them, they will suddenly become a whole world.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 281.


“A network with six elements is not a network. It is a mere network embryo. For typical networks, we would need to think to the 1000th order, or maybe to the 10,000th. This is clearly impossible. We have to make things simpler. Some of the most brilliant human minds have been involved in this simplification for some time now. The result is called game theory.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 296.


“Probability and thermodynamics in their classical interpretations assume the absence of strong and conditional interactions between the participants of the observed network.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. P. 303.


“Life itself, with all its decision points and multi-landscapes, and all the possible but mostly not followed pathways, can be imagined as a network. We have very few major decision points, and a large number of small ones. At the big decision points, we might go in a thousand directions, while at the small decision points, we may only select from a very few options.” Csermely, Peter. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. 2006. Springer Verlag. 308.


“Determining the average clustering coefficients of metabolic networks of 43 different organisms established that all were an order of magnitude larger than expected for a scale-free network of similar size. This suggested high modularity of these networks.” Jurisica, Igor & Dennis Wigle. Knowledge Discovery in Proteomics. 2006. Chapman & Hall. P. 107.


“We live in a world of networks, and, moreover, networks are the core of our civilization. Nowadays, in the epoch of economic, political, and cultural globalizaiton, one can see this with perfect clarity. Thus, the world of networks is our inevitable future, for better or worse.

“We are in a period of revolution in network science. Communication networks, the WWW, the Internet, peer-to-peer networks, genome, chemical reaction networks, catalytic networks, nets of metabolic reactions, protein networks, idiotypic networks, neural networks, signaling networks, artery nets, transportation networks, river networks, ecological and food webs, social networks, networks of collaborations, terrorist networks, nets of citations in scientific literature, telephone call graphs, mail networks, power grids, relations between enterprises, nets of ownership, networks of influence, the Word Web, electronic circuits, nets of software components, landscape networks, ‘geometric’ networks ...– what will be next?

“The progress is so immediate and astounding that we actually face a new science based on a new concept, and, one may even say, on a new philosophy: the natural philosophy of a small world. Old ideas from mathematics, statistical physics, biology, computer science, and so on take quite a new form in application to real evolving networks. Let us list some of basic points of this new conception:

● Evolving networks are one of the fundamental objects of statistical physics.
● Evolving networks self-organize into complex structures.
● The result of this self-organization is nets with the crucial role of highly connected vertices. The entire spectrum of connections is important in networks with such an architecture, but just the highly connected vertices determine the topology and basic properties of the networks, stability, transmission of interactions, spread of diseases, and so on.
● These networks have several levels of structural organization, several distinct scales: the local structure of connections of a vertex, the structure of connections in its environment, and the long-range structure of a network. Each of these levels determines a distinct set of properties of the networks.
● Networks are extremely ‘compact’ objects without well defined metric structure. Their organization varies between a tree-like form and a highly correlated structure, rich in loops, which are the two faces of a network.

“More than 40 years ago, in 1960, Erdös and Rényi wrote their seminal paper ‘On the evolution of random graphs,’ which is one of the starting points of mathematical random graph theory. What Erdös and Rényi called ‘random graphs’ were simple equilibrium networks with the Poisson distribution of connections. What they called ‘evolution’ was, actually, their construction procedure for these equilibrium graphs. Major recent achievements in network science are related to the transition to the study of the evolving, self-organizing networks with fat-tailed, non-Poisson, distributions of connections.

“Most basic networks, both natural and artificial, belong to this class: the Internet, the WWW, networks of protein interactions, and many, many others. As such, the impressive recent progress in this field represents a significant step towards understanding the most exciting networks of our world: the Internet and WWW, and basic biological networks. It is a step towards understanding one of the few fundamental objects of the Universe: a network.” Dorogovtsev, S.N. & J.F.F. Mendes. Evolution of Networks: From Biological Nets to the Internet and WWW. 2003. Oxford University Press. Pp. 219-20.


“Castells points to the emergence of ‘timeless time,’ a phenomenon that is created by hypertext and other new multimedia features, like hyperlinks, message permutations, and image manipulations, that destroy what was historically perceived as the natural sequence and time ordering of events (p. 462). These communication forms alter the way organizations, people, and the rest of the world are experienced. As Castells says, ‘All messages of all kinds become enclosed in the medium, because the medium has become so comprehensive, so diversified, so malleable, that it absorbs in the same multimedia text the whole of human experience, past, present, and future’ (p. 373).” Monge, Peter. & N. Contractor. Theories of Communication Networks. 2003. Oxford University Press. P. 5. Subquotes are from Castells, Manuel. 1996. The Rise of the Network Society (Vol. 1. The Information Age: Economy, Society and Culture). Blackwell Publishers. Pages 462, 373.


“Holland argues that one major theme runs through the various notions of emergence: ‘In each case there is a procedure for freely generating possibilities, coupled to a set of constraints that limit those possibilities.’” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. P. 13. Subquote is from Holland, John. 1997. Emergence: From Chaos to Order. Perseus Books. P. 122.


“At the knowledge level, the symbols or concepts acquire meaning through their relation to other concepts. These formalisms imply that knowledge can only be understood within a network of other knowledge concepts.” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. P. 91.


“Mutual causality is another key feature of the self-organizing system. The proposal that current levels of X can be partially predicted by prior levels of Y and current levels of Y can be partially predicted by prior levels of X captures a significant portion of the dynamism of evolutionary process. In the context of knowledge networks this type of mutual causality is reified again in the relationship between knowledge (X) and people (Y). On one hand, once people decide to share their knowledge with others, the knowledge becomes independent of the provider, and becomes information circulated in the network that can be used as raw material to generate more knowledge. Although we cannot say for sure what type of new knowledge will be created, accumulation and distribution of information and knowledge are necessary for the creation of new knowledge. On the other hand, as people are the actual inventors of new ideas, pooling together the intelligence of better informed and motivated people can spin off much more new knowledge.

“The third condition states that maintaining a self-organized state requires importing energy into the system. Following the law of requisite variety as first proposed by Ashby, the level of internal complexity of a system has to match the level of complexity of the external environment if it is to deal with the challenges posed by that environment.” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. P. 96.


“He [Polanyi, also Granovetter] observed that many theorists treated social institutions as if they were independent entities that were unconnected to other social institutions. He called this the ‘undersocialized’ (or unembedded) view of economic action and argued that it represented one pole of a continuum on which various social theories could be placed. The other end of the continuum was anchored by the notion of ‘oversocialized’ (or overembedded), which focused on the fact that many human and organizational economic actions were theorized to be extensively if not completely constrained by the social structures in which they are located. This view did not grant individuals or organizations much autonomy.” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. P. 148.


“Missing in the contagion model is the typical ebb and flow of messages through networks that typifies human communication. In human interaction messages containing differing ideas, values, and attitudes flow back and forth among people as they negotiate resolutions. The most typical outcome is modifications to the different positions each person held at the outset of the contagion process, modifications that influence both contaminators and the contaminated.” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. Pp. 184-5.


“In summary, transactive memory theory demonstrates that domains of knowledge are distributed throughout human and technical repositories in knowledge networks.” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. P. 203.


“The generative mechanism posited by cognitive consistency is that the number of transitive triads in which they are embedded influences an actor’s attributes. Thus, as we shall see below, according to cognitive consistency theory, an individual’s satisfaction at work is influenced by the extent to whcih the individual is embedded in a large number of transitive triad relations. If the relation is a friendship relation, cognitive consistency theory argues that individuals are satisfied when their friends are friends with one another.” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. Pp. 204-5.


“... consistency theories have also been utilized to address the ongoing debate about differences between actual and perceived communication. Freeman suggested that consistency theories offer a systematic explanation for differences between actual and self-report data on communication. He argued that individuals’ needs to perceive balance in observed communication networks help explain some of the errors they make in recalling communication patterns. Using experimental data collected by De Soto, Freeman found that a large proportion of the errors in subjects’ recall of networks could be attributed to their propensity to ‘correct’ intransitivity, a network indicator of imbalance, in the observed network.” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. P. 207. Reference is to Freeman, L.C. 1992. “Filling in the blanks: A theory of cognitive categories and the structure of social affiliation.” Social Psychology Quarterly, 55, 118-127.


“‘Commensalism refers to competition and cooperation between similar units, whereas symbiosis refers to mutual interdependence between dissimilar units.’” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. P. 257. Quote is from Aldrich, H. 1999. Organizations Evolving. Sage. P. 298.


“The first problem is the fact that the vast majority of network research is atheoretical. One reason for this is that there are very few explicit theories of social networks. Another reason is that researchers are generally not cognizant of the relational and structural implications inherent in various social theories. Even research that does employ theory typically does so without much attention to the network mechanisms implicit in the theories.

“A second problem with network research is that most scholars approach networks from a rather myopic, single-level perspective, which is reflected in the fact that almost all published research operates at a single level of analysis. Thus, they tend to focus on individual features of the network such as density. For the most part, researchers tend to ignore the multiple other components out of which most network configurations are composed, structural components from multiple levels of analysis such as mutuality, transitivity, and network centralization. Employing single levels of analysis is not inherently wrong; it is simply incomplete. Importantly, these components suggest different theoretical mechanisms in the formation, continuation, and eventual reconfiguration of networks. Typically, better explanations come from research that utilizes multiple levels of analysis.

“The third problem centers on the fact that most network research focuses on the relatively obvious elementary features of networks such as link density and fails to explore other, more complex properties of networks such as attributes of nodes or multiplex relations. But the members of networks often possess interesting theoretical properties, which help to shape the configurations in which they are embedded, and networks are themselves often tied to other networks. Traditional analyses typically account for relatively simple, surface features of networks and ignore these more subtle and sophisticated structural characteristics inherent in many networks.

“The final problem is that most network research tends to use descriptive rather than inferential statistics. The reason for this is that network relations contain inherent dependencies that typically do not exist in traditional attribute data. These dependencies invalidate the assumption of independent observations, which forms the basis for much of the traditional social science research on attributes.” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. Pp. 293-4.


“The future of network theory and research is nothing if not highly promising. We have come to realize at the beginning of the twenty-first century that we live in a highly connected world and society where the structural interconnections in large part determine what we can and cannot do. As we indicated at the beginning of the book, the amount of network theorizing and research has begun to grow geometrically in the past fifteen years.” Monge, Peter. & Noshir Contractor. 2003. Theories of Communication Networks. Oxford University Press. P. 326.


“As difficult as collective cooperation can be to generate and sustain in the absence of an effective central government or well-functioning markets, it can and does happen, not only in the international arena but also at the community, company, and household level. Although the conditions required for cooperation to emerge successfully among selfish decision makers are still a matter of debate, a torrent of theoretical and empirical work over the past two decades has shed considerable light on the matter. At the core of all these explanations lie two essential requirements. First, individuals must care about the future. And second, they must believe that their actions affect the decisions of others.” Watts, Duncan. Six Degrees: The Science of a Connected Age. 2003. W.W. Norton. Pp. 216-7.


“Networks that are not connected enough, therefore, prohibit global cascades because the cascade has no way of jumping form one vulnerable cluster to another. And networks that are too highly connected prohibit cascades also, but for a different reason: they are locked into a kind of stasis, each node constraining the influences of any other and being constrained itself.” Watts, Duncan. Six Degrees: The Science of a Connected Age. 2003. W.W. Norton. P. 241.
 

“Conversely, the scale-free topology does not require a fine-tuning of the system parameters to attain robustness. This topology allows the existence of robust dynamics in heterogeneous networks. Furthermore, the dynamics of scale-free networks can change if the highly connected elements are perturbed. Thus, scale-free networks operating in the robust regime exhibit both, robustness and sensitivity to perturbations. The above results suggest that the dynamical robustness of a wide variety of biological processes may be a direct consequence of the network architecture. The ubiquity of scale-free networks in nature with a topological parameter 2<γ<3 could be the result of evolutionary processes. Such a large class of networks, whose key dynamical processes are robust, may have an essential evolutionary advantage in possessing a larger parameter space to adapt to new environmental conditions. In our opinion, it would be surprising if nature did not exploit this convenient dynamical property of the scale-free topology.” Aldana, Maximino & Philippe Cluzel. “A Natural Class of Robust Networks.” Proceedings of the National Academy of the USA. July 22, 2003. Volume 100, no. 15, pp. 8710-8714. P. 8712. [γ is the negative exponent of the degree of a node giving its probability of occurrence in the network.]


“We will first consider feedforward networks, so named because node activity flows unidirectionally from input nodes to output nodes, without any feedback connections. Recurrent networks, i.e., networks with feedback connections, will be considered later.” Enquist, Magnus & Shefano Ghirlanda. 2005. Neural Networks and Animal Behavior. Princeton University Press. P. 39.


“Feedforward networks can realize any map with fixed input-output relationships but not maps in which the output to a given input depends on previous inputs.” Enquist, Magnus & Shefano Ghirlanda. 2005. Neural Networks and Animal Behavior. Princeton University Press. P. 43.


“The key architectural element in recurrent networks is feedback loops, or recurrent connections. This means that the activity of a node at a given time can be influenced by the activity of the same node at previous times. Usually this influence is mediated by the activity of other nodes at intervening times. In other words, while in feedforward networks only the weights serve as memory of the past, in recurrent networks the activity of nodes is also a source of memory.” Enquist, Magnus & Shefano Ghirlanda. 2005. Neural Networks and Animal Behavior. Princeton University Press. Pp. 43-4.
 

“Unlike for metabolic and transcriptional regulatory networks, well-defined hierarchical thinking has not emerged for signaling networks. The notions of modules and motifs are developing. However, a hierarchy similar to operons, regulons, and stimulons in transcriptional regulatory networks has not yet been delineated. In addition to motifs and modules, the concept of a cross-talk is often used to describe interactions in signaling networks. This notion [is] based on a signal’s ability to propagate beyond its ‘primary’ channel or pathway into another.” Palsson, Bernhard. 2006. Systems Biology: Properties of Reconstructed Networks. Cambridge University Press. P. 79.


“The human genome has a repertoire of approximately 25,000 genes, each with an average of three unique transcripts. A human being, comprising 1014 cells containing more than 200 different cell types, will develop from a fertilized egg. The coordination of this developmental process and the subsequent organism’s homeostatic mechanisms is achieved through signaling networks. This signaling network includes genes for 1,543 signaling receptors, 518 protein kinases, and approximately 150 protein phosphatases. These components of the human signaling network result in the activation (or inhibition) of the 1,850 transcription factors in the nucleus that in turn form a transcriptional regulatory network.” Palsson, Bernhard. 2006. Systems Biology: Properties of Reconstructed Networks. Cambridge University Press. Pp. 79-80.


“Network properties and functional states are often referred to as emergent properties since they do not depend on the functions of any particular component, but ‘emerge’ from the interactions of the components functioning together.” Palsson, Bernhard. 2006. Systems Biology: Properties of Reconstructed Networks. Cambridge University Press. P. 201.


“When you regard a living system you always find a network of processes or molecules that interact in such a way as to produce the very network that produced them and that determine its boundary. Such a network I call autopoietic. Whenever you encounter a network whose operations eventually produce itself as a result, you are facing an autopoietic system. It produces itself. The system is open to the input of matter but closed with regard to the dynamics of the relations that generate it.” Luisi, Pier Luigi. 2006. The Emergence of Life: From Chemical Origins to Synthetic Biology. Cambridge University Press. P. 158. [Quoting Maturana in piece by Poerksen (2004)]

“If two networks are exchanging information and learning from each other, they can synchronise.” Kinzel, Wolfgang. “Theory of Interacting Neural Networks.” Pp. 199-217. From Handbook of Graphs and Networks from the Genome to the Internet. Bornholdt, Stefan & Heinz Schuster, Editors. 2003. Wiley-VCH. P. 215.


“An autocatalytic set (ACS) is a subgraph, each of whose nodes has at least one incoming link from a node belonging to the same subgraph.” Jain, Sanjay & Sandeep Krishna. “Graph Theory and the Evolution of Autocatalytic Networks.” Pp. 355-395. From Handbook of Graphs and Networks from the Genome to the Internet. Bornholdt, Stefan & Heinz Schuster, Editors. 2003. Wiley-VCH. P. 363.
 

“In practice, finally, constituent units of claim-making actors often consist not of living, breathing whole individuals but of groups, organization, bundles of social ties, and social sites such as occupations and neighborhoods. Actors consist of networks deploying partially shared histories, cultures, and collective connections with other actors. The volunteers who trained in Oxford, Ohio, during June 1964 were joining and transforming a well-articulated network of activists.

“Such actors, however, almost never describe themselves as composite networks. Instead, they offer collective nouns; they call themselves workers, women, residents of X, or United Front Against Y. They attribute unitary intentions to themselves, and most often to the objects of their claims as well. They recast social relations and network processes as individuals and individually deliberated actions.” Tilly, Charles. Identities, Boundaries, & Social Ties. 2005. Paradigm Publishers. P. 61.


“Categorical inequality represents a special case of categorical relations in general. It is a particular but spectacularly potent combination within a small set of network configurations that have reappeared millions of times at different scales, in different settings, throughout human history. No one has codified our knowledge of these configurations. Provisional nominees for the basic set include the chain, the hierarchy, the triad, the organization, and the categorical pair:
1. The chain consists of two or more similar and connected ties between social sites–persons, groups, identities, networks, or something else.
2. Hierarchies are those sorts of chains in which the connections are asymmetrical and the sites systematically unequal.
3. Triads consist of three sites having similar ties to each other.
4. Organizations are well-bounded sets of ties in which at least one site has the right to establish ties across the boundary that bind members of internal ties.
5. A categorical pair consists of a socially significant boundary and at least one tie between sites on either side of it.”
Tilly, Charles. Identities, Boundaries, & Social Ties. 2005. Paradigm Publishers. P. 75.


“Granovetter’s famous application concerns job finding, where weak ties play an exceptional role because they connect job seekers with a much wider range of opportunities, on average, than do strong ones. Although subsequent research has shown that medium-weak ties, with their modicum of commitment, provide better-quality information than very weak ties, the broad distinction between effects of strong and weak ties has held up well to empirical scrutiny. Weak ties occupy important places in all sorts of large-scale coordination. Without weak ties, for example, most people would acquire very little information about current politics, medical innovations, or investment opportunities.” Tilly, Charles. Identities, Boundaries, & Social Ties. 2005. Paradigm Publishers. Pp. 77-8.

“Responsiveness implies an ability to adjust, perhaps dramatically, even to small environmental changes. Complementing the importance of effective response to environmental change when appropriate is the need for robustness to many other possible alterations. Robustness entails a lack of sensitivity to environmental variation, retaining the same behavior even when subject to large stimuli. Both properties are necessary for effective reaction and adaptation to environmental changes. The response of a system can be understood to be propagated through the network of connections, where the initial stimulus affects one or more nodes. Thus, the topology provides direct information about the nature of the response. It is natural in this context to characterize the size of a stimulus by the number of nodes that it initially affects and the response by the number that subsequently changes state.” Bar-Yam, Yaneer & Irving Epstein. “Response of Complex Networks to Stimuli.” The National Academy of Sciences of the USA. PNAS. March 30, 2004. Vol 101; No 13; 4341-4345. P. 4341.


“We first compare scale-free and random network topologies and show an increased sensitivity of the scale-free network to stimuli as compared with the random network. Still, simulations and analysis reveal that the increase in sensitivity is not as great as might be expected (or indeed might be needed by nature) for values of the scaling exponent (γ = 3) given by the standard theoretical model of scale-free networks. In particular, although the system can respond to smaller stimuli, the sensitivity is only increased by a factor of 2. Extending our analysis, we find that reducing the scaling exponent can increase the sensitivity. The sensitivity is almost unchanged down to an exponent of 2.5. However, within the range of 2.1 to 2.4, the sensitivity of the network increases dramatically. A review of the literature reveals that this domain corresponds to the phenomenological range of values observed in many natural networks.” Bar-Yam, Yaneer & Irving Epstein. “Response of Complex Networks to Stimuli.” The National Academy of Sciences of the USA. PNAS. March 30, 2004. Vol 101; No 13; 4341-4345. P. 4342.


“The complementary importance of robustness and sensitivity suggests that we reexamine previous analyses, which showed, with respect to removal of nodes from the network, that scale-free network topologies are robust to failure and sensitive to attack. Assuming, as is often done, that the former is an advantage, whereas the latter is a disadvantage, may be misleading in some contexts, because both characteristics can be advantageous if the sensitivity enables the system to respond effectively to environmental changes.” Bar-Yam, Yaneer & Irving Epstein. “Response of Complex Networks to Stimuli.” The National Academy of Sciences of the USA. PNAS. March 30, 2004. Vol 101; No 13; 4341-4345. P. 4341.


“Nestedness organizes complex coevolving networks in a specific way between highly specialized pairwise coevolution and highly diffuse coevolution. It results in both a core of taxa that may drive the evolution of the whole community, and in asymmetric interactions among species with different specialization levels. Our data do not indicate the presence of compartments suggestive of tight, parallel specialization. Rather, our results show that specialized species are frequently dependent on a core of generalist taxa. This macroscopic organization of coevolutionary interactions can be reduced neither to a collection of pairs of coevolving species, nor to a collection of subwebs made up of tightly integrated species.” Bascompte, Jordi, Pedro Jordano, Carlos Melian & Jens Olesen. “The Nested Assembly of Plant-animal Mutualistic Networks.” PNAS. August 5, 2003. Vol 100; No 16; 9383-9387. P. 9386.


“A key question regarding the evolvability of robust networks is whether one can reach highly robust networks starting from networks of low robustness through a series of small genetic changes. This is not self-evident. Recall that viable networks comprise a tiny fraction of all possible ones. They could be widely scattered in the space of all possible networks and occupy disconnected islands in this space. However, our analysis indicates precisely the opposite. The metagraph of viable networks has one ‘giant’ connected component that comprises most or all viable networks. Any two networks in this component can be reached from one another through gradual changes of one regulatory interaction at a time, changes that never leave the space of viable networks.” Ciliberti, Stefano, Olivier Martin & Andreas Wagner. “Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology.” PloS Computational Biology. February 2007. Vol. 3. Issue 2. e 15. Pps. 164-173. P. 168.


“‘... robustness is a property that allows a system to maintain its functions against internal and external perturbations.’” Kitano, Hiroaki. “Towards a theory of biological robustness.” Molecular Systems Biology. 3:137 18 September 2007. doi: 10:1038/msb4100179. Pps. 1-7. P. 1. Citing Kitano, H. “Biological robustness.” Nat. Rev. Genes. 5:826-837.


“Whereas homeostasis and stability are somewhat related concepts, robustness is a more general concept according to which a system is robust as long as it maintains functionality, even if it transits through a new steady state or if instability actually helps the system to cope with perturbations.” Kitano, Hiroaki. “Towards a theory of biological robustness.” Molecular Systems Biology. 3:137 18 September 2007. doi: 10:1038/msb4100179. Pps. 1-7. P. 1.


“In summary, whereas robustness is a general concept, homeostasis or stability can be considered as particular instances of robustness.” Kitano, Hiroaki. “Towards a theory of biological robustness.” Molecular Systems Biology. 3:137 18 September 2007. doi: 10:1038/msb4100179. Pps. 1-7. P. 1.


“In addition, most of the mathematics used to describe robustness are mostly based on control theory, which tend to focus on stability and performance of monostable systems.” Kitano, Hiroaki. “Towards a theory of biological robustness.” Molecular Systems Biology. 3:137 18 September 2007. doi: 10:1038/msb4100179. Pps. 1-7. P. 2.
 

“Some personal networks consist of a set of individuals, all of whom interact with one another; these are interlocking personal networks. In contrast, a radial personal network consists of a set of individuals who are linked to a focal individual but do not interact with one another. Radial personal networks are less dense and more open (openness is the degree to which a unit exchanges information with its environment), and thus allow the focal individual to exchange information with a wider environment. Such radial networks are particularly important in the diffusion of innovations because the links reach out into the entire system, while an interlocking network is more ingrown in nature.” Rogers, Everett M. Diffusion of Innovations. Fifth Edition. 2003. Free Press. P. 338.


“Why were weak ties so much more important than strong network links? Because an individual’s close friends seldom know much that the individual did not also know. One’s intimate friends are usually friends of each other’s, forming a close-knit clique (an interlocking personal network). Such an ingrown system is an extremely poor net in which to catch new information from one’s environment. Much more useful as a channel for gaining such information are an individual’s more distant (weaker) acquaintances, the people in one’s radial network. They are more likely to possess information that the focal individual does not already possess, such as about a new job or about an innovation. Weak ties connect an individual’s small clique of friends with another, distant clique. Thus, weak ties are often bridge links (defined as an individual who links two or more cliques in a system from his or her position as a member of one of the cliques), connecting two or more cliques.” Rogers, Everett M. Diffusion of Innovations. Fifth Edition. 2003. Free Press. Pp. 339-40.


“The weak-versus-strong-ties dimension is communication proximity, defined previously as the degree to which two individuals in a network have personal communication networks that overlap. Weak ties are low in communication proximity because they connect two individuals who do not share network links with a common set of other individuals. At least some degree of heterophily must be present in network links in order for the diffusion of innovations to occur. Low-proximity weak ties are often heterophilous, which is why they are so important in the diffusion process.” Rogers, Everett M. Diffusion of Innovations. Fifth Edition. 2003. Free Press. P. 340.


“A particular kind of connectionist network called a constraint-satisfaction network provides a rough model of individual interpretation formation. Rumelhart et al. define a constraint-satisfaction network as follows:

“‘a network in which each unit represents a hypothesis of some sort (e.g., that a certain semantic feature, visual feature, or acoustic feature is present in the input) and in which each connection represents constraints among the hypotheses. Thus, for example, if feature B is expected to be present whenever feature A is, there should be a positive connection from the unit corresponding to the hypothesis that A is present to the unit representing the hypothesis that B is present. Similarly, if there is a constraint that whenever A is present B is expected not to be present, there should be a negative connection from A to B. If the constraints are weak, the weights should be small. If the constraints are strong, then the weights should be large. Similarly, the inputs to such a network can also be thought of as constraints. A positive input to a particular unit means that there is evidence from the outside that the relevant feature is present. A negative input means that there is evidence from the outside that the feature is not present.’

“With each unit adjusting its activation (likelihood of being true) on the basis of the activations of neighbors and the strengths of the connections to those neighbors, such a network will eventually settle into a state in which as many of the constraints as is possible will be satisfied.” Hutchins, Edwin. Cognition in the Wild. 1995. MIT Press. Pp. 243-4. Subquote is from Rumelhart, D.E., Smolensky, P., McClelland, J.L, and Hinton, G.E. 1986. “Schemata and sequential thought processes in PDP models.” In J.L. McClelland, D.E. Rumelhart, and the PDP Research Group (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. volume 2. MIT Press.


“Networks are an essential ingredient in any complex adaptive system. Without interactions between agents, there can be no complexity. For example, the biological world comprises a massive hierarchy of networks: molecules interact in cells, cells interact in organisms, and organisms interact in ecosystems. The human body is a highly complex collection of networks within networks interacting with other networks, including the brain, nervous system, circulatory system, and immune system. If you took away the network structure of the human body, we would each be nothing more than a small box of chemicals and half a bathtub’s worth of water.

“The economic world likewise depends on networks. The earth is girdled by roads, sewers, water systems, electrical grids, railroad tracks, gas lines, radio waves, television signals, and fiber-optic cables. These provide the highways and byways of the matter, energy, and information flowing through the open system of the economy. The economy also contains massively complex virtual networks: people interact in companies, companies interact in markets, and markets interact in the gloabl economy. Just as in biology, the networks of the economic world are arranged in hierarchies of networks within networks.” Beinhocker, Eric. The Origin of Wealth: The Radical Remaking of Economics and What It Means for Business and Society. 2006. Harvard Business School Press. P. 141.


“Hierarchies are critical in enabling networks to reach larger sizes before diseconomies of scale set in. This is why so many networks in the natural and computer worlds are structured as networks within networks.” Beinhocker, Eric. The Origin of Wealth: The Radical Remaking of Economics and What It Means for Business and Society. 2006. Harvard Business School Press. P. 154.


“A fourth hierarchy type is the cumulative constitutive hierarchy. This structure is analogous to the cumulative aggregative hierarchy in that new units may join the hierarchy at a level above the fundamental one, but then become constituted into the succeeding levels. Despite having just considered the various levels in an organism’s construction as a simple constitutive hierarchy, such a structure is not actually sufficient to contain neatly all of the physical parts of an organism. For example, the extracellular matrix of metazoan bodies, which forms the ‘glue’ that holds most of them together, is not present in cells, and though partly manufactured by them, it is partly manufactured extracellularly. Extracellular matrix is a constituent of tissues, however, and therefore it is present in all higher levels of the hierarchy also–organs and so forth. Some fluids may be considered tissues, forming parts of hydrostatic organs, and hence are present in organ systems also. Thus an actual metazoan body is constructed as a cumulative constitutive hierarchy.” Valentine, James. On the Origin of Phyla. 2004. University of Chicago Press. P. 20.


“One reason that hierarchies have been so widely used by biologists to classify entities is that they are able to simplify and systematize complexity, ordering complicated entities so that they are more tractable to study. The ordering is possible because hierarchies forgive the differences among the entities within units; the entities within a given unit are assumed to be equivalent for purposes of classification at that level. Hierarchies also forgive differences among units at a given level. As described with great clarity by Medawar, when moving from level to level within a constitutive hierarchy from the basic constituents to realized units on higher levels, the scope of possible phenomena is progressively reduced, yet the richness of the realized phenomena is progressively increased. For example, elementary particles may form the ultimate base of a constitutive hierarchy. The potential number of things that can be formed from elementary particles is unimaginably large, presumably comprising everything in the universe and a lot that is not, and might involve unimaginably many potential hierarchies. As one proceeds up an actual hierarchy, however, the scope of the potential becomes limited. Cells are composed of elementary particles, but the scope of the possible entities on the cellular level and of the things that can be constructed from them is clearly greatly reduced from all the potential things that can be composed of elementary particles. On the other hand the complexity and richness of form and function encountered on the cellular level far outshines the drab sameness of collections of elementary particles. And so it goes up the hierarchy. In multicellular animals such as ourselves, the actual cells are only a narrow subset of potential cells, yet animals based on this subset are richly diverse in plan and detail.” Valentine, James. On the Origin of Phyla. 2004. University of Chicago Press. Pp. 20-21.


“Salthe discusses interlevel interactions in terms of (1) a focal level on which interest is centered, (2) the level immediately below, and (3) the level immediately above, together forming a ‘basic triadic system.’ Properties on the focal level are derived from a subset of the potential properties of the units on the level below, and include emergent properties not found on the lower level, but their ultimate importance is determined on the level above. The lower levels are the generators of focal-level properties, while the upper levels constrain them. In a very real sense, then, even the ultimate in reductionist explanation of a given level is incomplete, for it does not take into account the narrowing range of possibilities imposed at each higher level. In a somatic hierarchy, asymmetry implies that, for example, the functional requirements of a tissue constrain the ranges of cell types that compose it.” Valentine, James. On the Origin of Phyla. 2004. University of Chicago Press. P. 21.


“Although I introduce the idea of network power in the context of a discussion of contemporary globalization, I do not mean to suggest that it is a new phenomenon, but simply that it is one that has become more visible in the contemporary world. Networks, even global ones, are not new–and neither is the power present in the social interactions that generate them. Human history plentifully records intercultural trade, communication, and migration, spanning continents and millennia. But what is new about our age is the accelerated emergence of, and linkages among, these global networks. From trade to communication to domestic regulations, what was once mainly, even exclusively, ‘local’ is becoming increasingly global. More precisely, certain versions of local practices, routines, and symbols are being catapulted onto a global stage and offered as a means by which we can gain access to one another. They have become the standards by which we render each other’s actions comprehensible and comparable, and through which we are enabled to engage in forms of beneficial cooperation, such as the exchange of goods or ideas.

“Emerging global standards solve our problems–or at least compete to do so–and those standards that gain the most prominence become focal points in which we find the solution to the problem of global coordination. This ‘solution’ is, of course, double-edged: it offers coordination among diverse participants but it does so by elevating one solution above others and threatening the elimination of alternative solutions to the same problem. Inherent in the use of any standard is a tension between the cooperation that it allows users to enjoy and the check on innovation that it also imposes, since innovation would constitute a break in an ongoing cooperative regime.

“This double-edged aspect allows us to make sense of a seeming paradox at the heart of debates over globalization. On the one hand, globalization is often celebrated as an advance of human freedom in which individuals become ever more able to lead lives of their own choosing. Transnational flows of money, goods, and ideas accompany an increasingly liberal international order in which (it is claimed) individuals are ever freer to participate in a global economy and culture. At the same time, the complaint that globalization is based on power has become widespread, and is developed especially pointedly in recent accusations of ‘empire.’ At the center of these seemingly contradictory claims lies a difficulty in untangling voluntary choice-making from coercion, with allegations of both frequently being attached to the same action. For example, the choices of people to learn English or of nations to join the World Trade Organization (WTO) may seem on the face of it, well reasoned, freely made choices. Yet it is also argued that these choices stem from a kind of domination that they may in turn reinforce, reflecting the systemic power of already privileged actors or institutions. The idea of network power allows us to maintain our common-sense view of people as reasonable, choosing agents while simultaneously allowing that those doing the choosing may be subject to a form of external compulsion.” Grewal, David. Network Power: The Social Dynamics of Globalization. 2008. Yale University Press. Pp. 4-5.


“A standard defines the particular way in which a group of people is interconnected in a network. It is the shared norm or practice that enables network members to gain access to one another, facilitating their cooperation. A standard must be shared among members of the network to a sufficient degree that they can achieve forms of reciprocity, exchange, or collective effort. Consider, for example, networks of English speakers, Internet chat room participants, consumers in the Euro zone, or people who use the metric system. In every case, a standard is central to the existence of the network, serving as a convention common to all its members.” Grewal, David. Network Power: The Social Dynamics of Globalization. 2008. Yale University Press. P. 21.


“Another way to rephrase the general distinction is as follows: Mediocristan is where we must endure the tyranny of the collective, the routine, the obvious, and the predicted; Extremistan is where we are subjected to the tyranny of the singular, the accidental, the unseen, and the unpredicted.” Taleb, Nassim. The Black Swan: The Impact of the Highly Improbable. 2007. Random House. P. 35.


“Australian Aboriginal people, writes Bruce Chatwin, imagine their country not as a surface area that can be divided into blocks but as an ‘interlocking network’ of lines or ‘ways through’. ‘All our words for ‘country’, Chatwin’s Aboriginal interlocutor told him, ‘are the same as the words for ‘line’‘. These are the lines along which ancestral beings sang the world into existence in the Dreaming, and they are retraced in the comings and going, as well as the singing and storytelling, of their contemporary reincarnations. Taken together, they form a tangle of interwoven and complexly knotted strings. But is this tangle really a network, as Chatwin claims? It is indeed something like a net in its original sense of an open-work fabric of entwined threads or cords. It was in this sense, for example, that Gottfried Semper – in his essay of 1860 to which I referred in the last chapter – wrote of the ‘invention of the network’ among primitive people who made and used it for fishing and hunting. But through its metaphorical extension to the realms of modern transport and communications, and especially information technology, the meaning of ‘the net’ has changed. We are now more inclined to think of it as a complex of interconnected points than of interwoven lines. For this reason I find Chatwin’s characterization of Aboriginal country slightly misleading. It is more a meshwork than a network.” Ingold, Tim. Lines: A Brief History. 2007. Routledge. P. 80.


“Building an integration network involves setting up mental spaces, matching across spaces, projecting selectively to a blend, locating shared structures, projecting backward to inputs, recruiting new structure to the inputs or the blend, and running various operations in the blend itself. We will talk about these operations in sequence, but it is crucial to keep in mind that any of them can run at any time and that they can run simultaneously. The integration network is trying to achieve equilibrium. In a manner of speaking, there is a place where the network is ‘happy.’ Context will typically specify some conditions of the equilibrium, as when we are instructed to find a solution to the riddle of the Buddhist Monk. The network will achieve equilibrium if structure comes up in the blend that projects back automatically to the inputs to yield the existence of the special point on the path. More generally, what counts as an equilibrium for the network will depend on its purpose, but also on various internal constraints on its dynamics.” Fauconnier, Gilles & Mark Turner. The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. 2002. Basic Books. Pp. 44-5.


“... we present an overview of why and how the social network approach might be useful for behavioral ecology. We first note four important aspects of SNS [“social network structure”] that are commonly observed, but relatively rarely quantified: (1) that within a social group, differences among individuals in their social experiences and connections affect individual and group outcomes; (2) that indirect connections can be important (e.g., partners of your partners matter); (3) that individuals differ in their importance in the social network (some can be considered keystone individuals); and (4) that social network traits often carry over across contexts (e.g., SN position in male-male competition can influence later male mating success).” Sih, Andrew, S. Hanser & K. McHugh. “Social network theory: new insights and issues for behavioral ecologists.” Behav Ecol Sociobiol (2009) 63:975-988. P. 975.


“Any whole-cell metabolic model contains a number of transport reactions for the uptake of nutrients and excretion of byproducts. Consequently, we may systematically sample among all possible environments captured by the model through varying the constraints on uptake reactions. This analysis suggests that optimal metabolic flows are adjusted to environmental changes through two distinct mechanisms. The more common mechanism is ‘flux plasticity’, involving changes in the fluxes of already active reactions when the organism is shifted from one growth condition to another. For example, changing from glucose- to succinate-rich media altered the flux of 264 E. coli reactions by more than 20%. Less commonly, environmental changes may induce ‘structural plasticity’, resulting in changes to the metabolism’s active wiring diagram, turning on previously zero-flux reactions and inhibiting previously active pathways. For example, when shifting E. coli cells from glucose- to succinate-rich media, 11 previously active reactions were turned off completely, while nine previously inactive reactions were turned on.

“The ‘metabolic core’ is the set of reactions found to be active (carrying a non-zero metabolic flux) in all tested environments. In recent computational experiments where more than 30,000 possible environments were sampled, the metabolic core contained 138 of the 381 metabolic reactions in the model of H. pylori (36.2%), 90 of 758 in E. coli (11.9%) and 33 of 1172 in S. cerevisiae (2.8%). While these reactions respond to environmental changes only through flux-based plasticity, the remaining reactions are conditionally active, being turned on only in specific growth conditions.” Almaas, Eivind. “Biological impacts and context of network theory.” 2007. The Journal of Experimental Biology 210.1548-1558. P. 1556.


“While degree metrics gauge the extent to which a node is directly connected to all other nodes in the network, betweenness measures the extent to which a node is directly connected only to those other nodes that are not directly connected to each other. That is, it measures the extent to which a node serves as an intermediary ‘between’ other nodes in the network.” Monge, Peter and N. Contractor. Theories of Communication Networks. 2003. Oxford University Press. P. 38.


“With the burgeoning interest in networks, wherein flows are accorded parity with states (nodes), it becomes likely that the groundwork in thermodynamics may soon shift in favor of flow variables.” Ulanowicz, Robert. A Third Window: Natural Life beyond Newton and Darwin. 2009. Templeton Foundation Press. P. 41.


“Newman has highlighted another aspect of the deep substructure of complex networks. In some of them, highly connected nodes show a greater-than-average propensity to have links with other highly connected nodes, forming what has become dubbed a ‘rich club’. This phenomenon is known as assortative mixing. Obviously, the existence of rich clubs in social, economic, and professional networks could have an enormous impact on the way society functions–it might imply, for example, that the ‘rich club’ members are able to share privileged information that percolates only slowly into the rest of the network. Newman has shown that while neither the random graphs of Erdos and Renyi nor the scale-free networks grown from Barabasi and Albert’s ‘preferential attachment’ model has rich clubs, many real-world social networks do, including the collaboration networks of scientists, film stars, and company directors. On the other hand, some natural networks are negatively assortative: they show fewer than expected links between ‘rich’ nodes. This is true of the Internet, and to a small degree of the WWW; and it also applies to the protein interaction network of yeast, the neural network of the nematode worm, and the marine food web. In other words, there is something different about social networks compared to others, either technological or biological: we humans seem disposed towards forming rich clubs. Newman has argued that this is because of the strong tendency of social networks to partition into communities, which is less prevalent in other webs.” Ball, Philip. Nature’s Patterns: A Tapestry in Three Parts - Branches. 2009. Oxford University Press. P. 171.


“We have outlined the reasons for which networks are set to become a major paradigm in biology. Indeed the advantages of describing the interactions between the molecular components of a cell in terms of networks are becoming increasingly recognized. This approach enables [us] to illustrate in an efficient manner the hierarchical organization of a cell, which consists of intertwined networks on a wide range of scales, from the interactions between individual metabolites or proteins to the interplay between metabolic and signaling networks.” Buchanan, M. & G. Caldarelli, Ed. Networks in Cell Biology. 2010. Cambridge University Press. P. 13.


“Two properties of real networks have generated considerable attention. First, many networks are fundamentally modular: one can easily identify groups of nodes that are highly interconnected with each other, but have only a few or no links to nodes outside of the group to which they belong to. Each module is expected to perform an identifiable task, separate from the function of other modules....”

“At the same time, many networks of scientific or technological interest, ranging from biological networks to the World Wide Web have been found to be scale free: there is no well defined ‘connectivity scale’ that approximates the degree of most nodes in the system....


“In order to bring modularity, the high degree of clustering and the scale-free topology under a single roof, we have proposed that modules combine with one another in a hierarchical manner, generating what we call a hierarchical network.” Buchanan, M. & G. Caldarelli, Ed. Networks in Cell Biology. 2010. Regan, E.R. “Hierarchical modularity in biological networks: the case of metabolic networks.” Pp. 117-134. Cambridge University Press. Pp. 119-20.


“Networks are commonly used in biology to represent molecular mechanisms occurring in the cell, from protein-protein interactions to enzymatic reactions (metabolic networks) and gene regulations (gene regulatory networks). The use of tools derived from graph theory has led to the successful characterization of some aspects of the topology and organization of these networks and the intracellular mechanisms they represent. The study of intracellular networks so far has revealed their small-worldness and scale-freeness, as well as the existence of network motifs, and a strongly modular organization. Network representations are also increasingly used to characterize the function of the objects (genes, proteins, metabolites) they interconnect.” Mazurie, A., D. Bonchev, B. Schwikowski & G. Buck. “Evolution of metabolic network organization.” BMC Systems Biology. 2010, 4:59. P. 1.


“Networks are also used in comparative studies to highlight the differences and similarities existing in the organization of the intracellular mechanisms of multiple species. Studies have been published that compare the topological features of metabolic networks for taxa sampled from all three kingdoms of life. These investigations shed light on how these metabolic networks differ among the archaea, bacteria and eukarya, as a result of natural selection processes. The results show that while some properties are shared by all taxa (e.g., their metabolic networks are all scale-free), bacteria species distinguish themselves over archaea and eukarya by having a shorter network average path length. Metabolic networks of archaea species also exhibit a lower average clustering coefficient, betweenness centrality and scale-freeness than those of other species. Finally, the organization of the metabolism of species appears to be impacted by the species lifestyle and phenotype; e.g., by the variability of their habitat, or their optimal growth temperature. Metabolic networks of modern organisms have also been compared to inferred ancestral ones. These studies indicate that niche specialization and extreme environments tend to decrease the modularity of the metabolic networks of taxa.” Mazurie, A., D. Bonchev, B. Schwikowski & G. Buck. “Evolution of metabolic network organization.” BMC Systems Biology. 2010, 4:59. Pp. 1-2.


“Addressing the effect of evolutionary pressures on the metabolism of species, we used a higher representation of metabolisms–the Network of Interacting Pathways, or NIP–focusing on the logic of metabolic organization, rather than on the details of the underlying molecular mechanisms. In this study we demonstrate that specific aspects of the structure and complexity of these NIPs are discriminative of species from different lineages and lifestyle; i.e., species under distinct evolutionary pressures.

“We found that species that evolved to accommodate more complex lifestyles; e.g., eukarya compared to prokarya, multicellular eukarya compared to unicellular eukarya, motile bactera compared to immobile bacteria, developed not only bigger but also more structured metabolic networks. Communication between pathways is typically enhanced by the addition of hubs (highly connected pathways, which convert metabolites for a broader range of input and output pathways) and switch-boards (centrally connected pathways, which capture a large fraction of the whole metabolite traffic). As for the transition from anaerobic to aerobic bacteria, we observed only enlargement of networks with the inclusion of additional metabolic pathways, while the organization and complexity of the cross talk between pathways remained mostly unchanged. The reorganization of metabolic networks is shown to be heterogeneous, with specific categories of pathways being promoted to more central or connected locations. Thus, we demonstrated the increased importance of lipid metabolism for multi-cellular eukarya, glycan and xenobiotics metabolism for motile bacteria, and lipid, amino acid and xenobiotics metaboism for aerobic bacteria. Conversely, we found that the decrease in complexity of the metabolism of host-associated bacteria significantly impacted carbohydrate and energy metabolism” Mazurie, A., D. Bonchev, B. Schwikowski & G. Buck. “Evolution of metabolic network organization.” BMC Systems Biology. 2010, 4:59. P. 8.


“The appearance of hardwired connections allowed larger and more complex networks to arise. Party networks work well at the molecular level because molecules travel extremely fast, but such networks lose steam as their size increases.” Vertosick, Frank. The Genius Within: Discovering the Intelligence of Every Living Thing. 2002. Harcourt. Pp. 187-8.


“Critics of Darwinian evolution, including the proponents of the quasi-religious ‘intelligent design’ school, argue that natural selection alone can’t generate the complexity we see in the modern biosphere. They’re wrong–natural selection does generate complexity, but it does so only by serving as a learning rule in a network paradigm.” Vertosick, Frank. The Genius Within: Discovering the Intelligence of Every Living Thing. 2002. Harcourt. P. 214.


“Although the mechanics and time scales may differ, all biological learning, whether in the simplest cell or the smartest brain, is a local fitness competition operating on the connection weights of networks. We can go one step further and define evolution generically as that process that trains the connection weights in living networks, thereby allowing them to acquire and recall patterned sets of environmental data.” Vertosick, Frank. The Genius Within: Discovering the Intelligence of Every Living Thing. 2002. Harcourt. P. 219.


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