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