***** To join INSNA, visit http://www.insna.org *****
omg, its almost rosh ha-shonnah
Barry Wellman
A vision is just a vision if it's only in your head
Step by step, link by link, putting it together
Streisand/Sondheim
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NetLab Network FRSC INSNA Founder
Distinguished Visiting Scholar Social Media Lab Ryerson University
Distinguished Senior Advisor University Learning Academy
http://www.chass.utoronto.ca/~wellman twitter: @barrywellman
NETWORKED: The New Social Operating System Lee Rainie & Barry Wellman
http://amzn.to/zXZg39
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Date: Mon, 4 Sep 2017 11:02:44 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
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To: "[utf-8] Barry" <[log in to unmask]>
Subject: [utf-8] Latest Complexity Digest Posts
Learn about the latest and greatest related to complex systems research. More at http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5ad652436e&e=55e25a0e3e
MetaZipf. A dynamic meta-analysis of city size distributions
The results from urban scaling in recent years have held the promise of
increased efficiency to the societies who could actively control the
distribution of their citiesÿÿ size. However, little evidence exists as to
the factors which influence the level of urban unevenness, as expressed by
the slope of the rank-size distribution, partly because the diversity of
results found in the literature follows the heterogeneity of analysis
specifications. In this study, I set up a meta-analysis of Zipfÿÿs law
which accounts for technical as well as topical factors of variations of
Zipfÿÿs coefficient. I found 86 studies publishing at least one empirical
estimation of this coefficient and recorded their metadata into an open
database. I regressed the 1962 corresponding estimates with variables
describing the study and the estimation process as well as
socio-demographic variables describing the territory under enquiry. A
dynamic meta-analysis was also performed to look for factors of evolution
of city size unevenness. The results of the most interesting models are
presented in the article, whereas all analyses can be reproduced on a
dedicated online platform. The results show that on average, 40% of the
variation of Zipfÿÿs coefficients is due to the technical choices. The
main other variables associated with distinct evolutions are linked to the
urbanisation process rather than the process of economic development and
population growth. Finally, no evidence was found to support the
effectiveness of past planning actions in modifying this urban feature.
Cottineau C (2017) MetaZipf. A dynamic meta-analysis of city size distributions. PLoS ONE 12(8): e0183919. http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=72ee8f1864&e=55e25a0e3e
Source: journals.plos.org (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d117e8a9f8&e=55e25a0e3e)
Network Analysis of Particles and Grains
The arrangements of particles and forces in granular materials and
particulate matter have a complex organization on multiple spatial scales
that range from local structures to mesoscale and system-wide ones. This
multiscale organization can affect how a material responds or reconfigures
when exposed to external perturbations or loading. The theoretical study
of particle-level, force-chain, domain, and bulk properties requires the
development and application of appropriate mathematical, statistical,
physical, and computational frameworks. Traditionally, granular materials
have been investigated using particulate or continuum models, each of
which tends to be implicitly agnostic to multiscale organization.
Recently, tools from network science have emerged as powerful approaches
for probing and characterizing heterogeneous architectures in complex
systems, and a diverse set of methods have yielded fascinating insights
into granular materials. In this paper, we review work on network-based
approaches to studying granular materials (and particulate matter more
generally) and explore the potential of such frameworks to provide a
useful description of these materials and to enhance understanding of the
underlying physics. We also outline a few open questions and highlight
particularly promising future directions in the analysis and design of
granular materials and other particulate matter.
Network Analysis of Particles and Grains
Lia Papadopoulos, Mason A. Porter, Karen E. Daniels, Danielle S. Bassett
Source: arxiv.org (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=8e06270b9b&e=55e25a0e3e)
Sampling of Temporal Networks: Methods and Biases
Temporal networks have been increasingly used to model a diversity of
systems that evolve in time; for example human contact structures over
which dynamic processes such as epidemics take place. A fundamental aspect
of real-life networks is that they are sampled within temporal and spatial
frames. Furthermore, one might wish to subsample networks to reduce their
size for better visualization or to perform computationally intensive
simulations. The sampling method may affect the network structure and thus
caution is necessary to generalize results based on samples. In this
paper, we study four sampling strategies applied to a variety of real-life
temporal networks. We quantify the biases generated by each sampling
strategy on a number of relevant statistics such as link activity,
temporal paths and epidemic spread. We find that some biases are common in
a variety of networks and statistics, but one strategy, uniform sampling
of nodes, shows improved performance in most scenarios. Our results help
researchers to better design network data collection protocols and to
understand the limitations of sampled temporal network data.
Sampling of Temporal Networks: Methods and Biases
Luis E C Rocha, Naoki Masuda, Petter Holme
Source: arxiv.org (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=569f338ae6&e=55e25a0e3e)
Hipsters on Networks: How a Small Group of Individuals Can Lead to an Anti-Establishment Majority
The spread of opinions, memes, diseases, and "alternative facts" in a
population depends both on the details of the spreading process and on the
structure of the social and communication networks on which they spread.
One feature that can change spreading dynamics substantially is
heterogeneous behavior among different types of individuals in a social
network. In this paper, we explore how anti-establishment nodes (e.g.,
hipsters) influence spreading dynamics of two competing products. We
consider a model in which spreading follows a deterministic rule for
updating node states in which an adjustable fraction pHip of the nodes in
a network are hipsters, who always choose to adopt the product that they
believe is the less popular of the two. The remaining nodes are
conformists, who choose which product to adopt by considering only which
products their immediate neighbors have adopted. We simulate our model on
both synthetic and real networks, and we show that the hipsters have a
major effect on the final fraction of people who adopt each product: even
when only one of the two products exists at the beginning of the
simulations, a very small fraction of hipsters in a network can still
cause the other product to eventually become more popular. Our simulations
also demonstrate that a time delay ÿÿ in the knowledge of the product
distribution in a population has a large effect on the final distribution
of product adoptions. Our simple model and analysis may help shed light on
the road to success for anti-establishment choices in elections, as such
success --- and qualitative differences in final outcomes between
competing products, political candidates, and so on --- can arise rather
generically from a small number of anti-establishment individuals and
ordinary processes of social influence on normal individuals.
Hipsters on Networks: How a Small Group of Individuals Can Lead to an Anti-Establishment Majority
Jonas S. Juul, Mason A. Porter
Source: arxiv.org (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=22f51d156a&e=55e25a0e3e)
The coevolution of networks and health
Historically, health has played an important role in network research,
and vice versa (Valente, 2010). This intersection has contributed to how
we understand human health as well as the development of network concepts,
theory, and methods. Throughout, dynamics have featured prominently. Even
when limited to static methods, the emphasis in each of these fields on
providing causal explanations has led researchers to draw upon theories
that are dynamic, often explicitly. Here, we elaborate a variety of ways
to conceptualize the relationship between health and network dynamics,
show how these possibilities are reflected in the existing literature,
highlight how the articles within this special issue expand that
understanding, and finally, identify paths for future research to push
this intersection forward.
The coevolution of networks and health
DAVID R. SCHAEFER, JIMI ADAMS
Network Science, Volume 5 / Issue 3, August 2017, pp 249 - 256
doi: 10.1017/nws.2017.24
Source: www.cambridge.org (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=73868b97b6&e=55e25a0e3e)
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Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.
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