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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.

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.

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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.

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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.

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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.

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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.

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