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SOCNET  September 2017

SOCNET September 2017

Subject:

selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 4 Sep 2017 09:10:41 -0400

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (196 lines)

*****  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
  _______________________________________________________________________
   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
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 4 Sep 2017 11:02:44 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
Reply-To: [log in to unmask]
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 cities 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 Zipfs law 
which accounts for technical as well as topical factors of variations of 
Zipfs 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 Zipfs 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  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)


==============================================
Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

You can contribute to Complexity Digest selecting one of our topics (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=2ce5e6cd71&e=55e25a0e3e ) and using the "Suggest" button.
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