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***** To join INSNA, visit http://www.insna.org ***** Hi all! There were a couple of judgment calls this week, so I went with my personal instinct and interest. I found the context dependency piece to be very interesting, and although perhaps a borderline case for network researchers who don't deal directly with complexity theories, I think everyone who works with networks has to grapple with the question of context specificity at some point, so I left it. Enjoy! Dawn ______________________________________ Dawn R. Gilpin, PhD Walter Cronkite School of Journalism & Mass Communication Arizona State University [log in to unmask] @drgilpin =========================== Social Relationships and the Emergence of Social Networks Alistair Sutcliffe, Di Wang and Robin Dunbar (2012) Journal of Artificial Societies and Social Simulation 15 (4) 3 http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=f867959be5&e=d38efa683e In complex social systems such as those of many mammals, including humans, groups (and hence ego-centric social networks) are commonly structured in discrete layers. We describe a computational model for the development of social relationships based on agents' strategies for social interaction that favour more less-intense, or fewer more-intense partners. A trust-related process controls the formation and decay of relationships as a function of interaction frequency, the history of interaction, and the agents' strategies. A good fit of the observed layers of human social networks was found across a range of model parameter settings. Social interaction strategies which favour interacting with existing strong ties or a time-variant strategy produced more observation-conformant results than strategies favouring more weak relationships. Strong-tie strategies spread in populations under a range of fitness conditions favouring wellbeing, whereas weak-tie strategies spread when fitness favours foraging for food. The implications for modelling the emergence of social relationships in complex structured social networks are discussed. -------------- Spatiotemporal correlations of handset-based service usages Hang-Hyun Jo, Márton Karsai, Juuso Karikoski and Kimmo Kaski EPJ Data Science 2012, 1:10http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=f53eb09beb&e=d38efa683e We study spatiotemporal correlations and temporal diversities of handset-based service usages by analyzing a dataset that includes detailed information about locations and service usages of 124 users over 16 months. By constructing the spatiotemporal trajectories of the users we detect several meaningful places or contexts for each one of them and show how the context affects the service usage patterns. We find that temporal patterns of service usages are bound to the typical weekly cycles of humans, yet they show maximal activities at different times. We first discuss their temporal correlations and then investigate the time-ordering behavior of communication services like calls being followed by the non-communication services like applications. We also find that the behavioral overlap network based on the clustering of temporal patterns is comparable to the communication network of users. Our approach provides a useful framework for handset-based data analysis and helps us to understand the complexities of information and communications technology enabled human behavior. -------------- A large-scale community structure analysis in Facebook Emilio Ferrara EPJ Data Science 2012, 1:9http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=4f656bce05&e=d38efa683e Understanding social dynamics that govern human phenomena, such as communications and social relationships is a major problem in current computational social sciences. In particular, given the unprecedented success of online social networks (OSNs), in this paper we are concerned with the analysis of aggregation patterns and social dynamics occurring among users of the largest OSN as the date: Facebook. In detail, we discuss the mesoscopic features of the community structure of this network, considering the perspective of the communities, which has not yet been studied on such a large scale. To this purpose, we acquired a sample of this network containing millions of users and their social relationships; then, we unveiled the communities representing the aggregation units among which users gather and interact; finally, we analyzed the statistical features of such a network of communities, discovering and characterizing some specific organization patterns followed by individuals interacting in online social networks, that emerge considering different sampling techniques and clustering methodologies. This study provides some clues of the tendency of individuals to establish social interactions in online social networks that eventually contribute to building a well-connected social structure, and opens space for further social studies. -------------- Evolution of Associative Learning in Chemical Networks McGregor S, Vasas V, Husbands P, Fernando C (2012) Evolution of Associative Learning in Chemical Networks. PLoS Comput Biol 8(11): e1002739. http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=4436eb6315&e=d38efa683e Whilst one may have believed that associative learning requires a nervous system, this paper shows that chemical networks can be evolved in silico to undertake a range of associative learning tasks with only a small number of reactions. The mechanisms are surprisingly simple. The networks can be analysed using Bayesian methods to identify the components of the network responsible for learning. The networks evolved were simpler in some ways to hand-designed synthetic biology networks for associative learning. The motifs may be looked for in biochemical networks and the hypothesis that they undertake associative learning, e.g. in single cells or during development may be legitimately entertained. -------------- Complexity and Context-Dependency Bruce Edmonds FOUNDATIONS OF SCIENCE 2012, http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=05d5bea130&e=d38efa683e It is argued that given the “anti-anthropomorphic” principle—that the universe is not structured for our benefit—modelling trade-offs will necessarily mean that many of our models will be context-specific. It is argued that context-specificity is not the same as relativism. The “context heuristic”—that of dividing processing into rich, fuzzy context-recognition and crisp, conscious reasoning and learning—is outlined. The consequences of accepting the impact of this human heuristic in the light of the necessity of accepting context-specificity in our modelling of complex systems is examined. In particular the development of “islands” or related model clusters rather than over-arching laws and theories. It is suggested that by accepting and dealing with context (rather than ignoring it) we can push the boundaries of science a little further. -------------- Complexity, Networks, and Non-Uniqueness Alan Baker FOUNDATIONS OF SCIENCE 2012, http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b84c86e715&e=d38efa683e The aim of the paper is to introduce some of the history and key concepts of network science to a philosophical audience, and to highlight a crucial—and often problematic—presumption that underlies the network approach to complex systems. Network scientists often talk of “the structure” of a given complex system or phenomenon, which encourages the view that there is a unique and privileged structure inherent to the system, and that the aim of a network model is to delineate this structure. I argue that this sort of naïve realism about structure is not a coherent or plausible position, especially given the multiplicity of types of entities and relations that can feature as nodes and links in complex networks. -------------- Global Civil Unrest: Contagion, Self-Organization, and Prediction Braha D (2012) Global Civil Unrest: Contagion, Self-Organization, and Prediction. PLoS ONE 7(10): e48596.http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=0801bb8a83&e=d38efa683e Civil unrest is a powerful form of collective human dynamics, which has led to major transitions of societies in modern history. The study of collective human dynamics, including collective aggression, has been the focus of much discussion in the context of modeling and identification of universal patterns of behavior. In contrast, the possibility that civil unrest activities, across countries and over long time periods, are governed by universal mechanisms has not been explored. Here, records of civil unrest of 170 countries during the period 1919–2008 are analyzed. It is demonstrated that the distributions of the number of unrest events per year are robustly reproduced by a nonlinear, spatially extended dynamical model, which reflects the spread of civil disorder between geographic regions connected through social and communication networks. The results also expose the similarity between global social instability and the dynamics of natural hazards and epidemics. _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.