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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.



Dawn R. Gilpin, PhD
Walter Cronkite School of Journalism & Mass Communication
Arizona State University
[log in to unmask]


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

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, 

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, 

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.

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


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


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 

PLoS ONE 7(10): 

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.

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