***** To join INSNA, visit http://www.insna.org ***** clearing up backlog Barry Wellman Step by step, link by link, putting it together--Streisand/Sondheim The earth to be spannd, connected by network--Walt Whitman It's Always Something--Roseanne Roseannadanna A day like all days, filled with those events that alter and illuminate our times--You Are There! _______________________________________________________________________ Director, NetLab Network FRSC Founder, International Network for Social Network Analysis NETWORKED: The New Social Operating System Lee Rainie & Barry Wellman https://urldefense.proofpoint.com/v2/url?u=http-3A__www.chass.utoronto.ca_-7Ewellman&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=Tn4i1t_2APG2TlaWDq63DBYxdZ0ZbIqGKYFBYKWpJVc&s=M01NgkrZZo8w1hc-teHALCFBN9vAoiAvTcVbkhs4cO8&e= https://urldefense.proofpoint.com/v2/url?u=http-3A__amzn.to_zXZg39&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=Tn4i1t_2APG2TlaWDq63DBYxdZ0ZbIqGKYFBYKWpJVc&s=TsBCCGI0FsA-MtcP3B3myLHFkWKb9PC67KVTF1IFay4&e= https://urldefense.proofpoint.com/v2/url?u=https-3A__en.wikipedia.org_wiki_Barry-5FWellman&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=Tn4i1t_2APG2TlaWDq63DBYxdZ0ZbIqGKYFBYKWpJVc&s=sQHnzg_hREwK3KOzphOJXeg_s7POrWru4XJeHBD3Feg&e= _______________________________________________________________________ ---------- Forwarded message ---------- Date: Mon, 12 Aug 2019 11:01:32 +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 https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dfdc1677c97-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=Tn4i1t_2APG2TlaWDq63DBYxdZ0ZbIqGKYFBYKWpJVc&s=nEes-PaOEvOC9Vcbvoy-zz_8LJ3k6f1XW2M-MeXbvyM&e= Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dd491847cac-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=Tn4i1t_2APG2TlaWDq63DBYxdZ0ZbIqGKYFBYKWpJVc&s=xe-a8vWKBvnDpCqi87PYCgCjAgRRvxX7KdShRQ2_ILI&e= Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters are the subjects of case studies, systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale are scarce. In this work, we use LinkedInÿÿs employment history data from more than 500 million users over 25 years to construct a labor flow network of over 4 million firms across the world, from which we reveal hierarchical structure by applying network community detection. We show that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated workers and financial performance, compared to traditional aggregation units. Furthermore, our analysis of the skills of educated workers reveals richer insights into the relationship between the labor flow of educated workers and productivity growth. We argue that geo-industrial clusters defined by labor flow provide useful insights into the growth of the economy. Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters Jaehyuk Park, Ian B. Wood, Elise Jing, Azadeh Nematzadeh, Souvik Ghosh, Michael D. Conover & Yong-Yeol Ahn Nature Communications volume 10, Article number: 3449 (2019) Source: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.nature.com&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=Tn4i1t_2APG2TlaWDq63DBYxdZ0ZbIqGKYFBYKWpJVc&s=bLpEer9DyG61FsEpVTrJ2urPtcfN8jpW3Go1Lq-OaRE&e= (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D7e9adc9e34-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=Tn4i1t_2APG2TlaWDq63DBYxdZ0ZbIqGKYFBYKWpJVc&s=ohDNodAMGR_a0J8OLRq2tOuL43-ggfrn_TD55PeXA_w&e= ) Fundamental Structures in Dynamic Communication Networks In this paper I introduce a framework for modeling temporal communication networks and dynamical processes unfolding on such networks. The framework originates from the realization that there is a meaningful division of temporal communication networks into six dynamic classes, where the class of a network is determined by its generating process. In particular, each class is characterized by a fundamental structure: a temporal-topological network motif, which corresponds to the network representation of communication events in that class of network. These fundamental structures constrain network configurations: only certain configurations are possible within a dynamic class. In this way the framework presented here highlights strong constraints on network structures, which simplify analyses and shape network flows. Therefore the fundamental structures hold the potential to impact how we model temporal networks overall. I argue below that networks within the same class can be meaningfully compared, and modeled using similar techniques, but that integrating statistics across networks belonging to separate classes is not meaningful in general. This paper presents a framework for how to analyze networks in general, rather than a particular result of analyzing a particular dataset. I hope, however, that readers interested in modeling temporal networks will find the ideas and discussion useful in spite of the paper's more conceptual nature. Fundamental Structures in Dynamic Communication Networks Sune Lehmann Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D8820d6feb6-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=Tn4i1t_2APG2TlaWDq63DBYxdZ0ZbIqGKYFBYKWpJVc&s=WzjVW0wa9esdkmFqUKtGNvNbHEi_oHJB-fK3bcgDKE8&e= ) Optimal foraging and the information theory of gambling At a macroscopic level, part of the ant colony life cycle is simple: a colony collects resources; these resources are converted into more ants, and these ants in turn collect more resources. Because more ants collect more resources, this is a multiplicative process, and the expected logarithm of the amount of resources determines how successful the colony will be in the long run. Over 60 years ago, Kelly showed, using information theoretic techniques, that the rate of growth of resources for such a situation is optimized by a strategy of betting in proportion to the probability of pay-off. Thus, in the case of ants, the fraction of the colony foraging at a given location should be proportional to the probability that resources will be found there, a result widely applied in the mathematics of gambling. This theoretical optimum leads to predictions as to which collective ant movement strategies might have evolved. Here, we show how colony-level optimal foraging behaviour can be achieved by mapping movement to Markov chain Monte Carlo (MCMC) methods, specifically Hamiltonian Monte Carlo (HMC). This can be done by the ants following a (noisy) local measurement of the (logarithm of) resource probability gradient (possibly supplemented with momentum, i.e. a propensity to move in the same direction). This maps the problem of foraging (via the information theory of gambling, stochastic dynamics and techniques employed within Bayesian statistics to efficiently sample from probability distributions) to simple models of ant foraging behaviour. This identification has broad applicability, facilitates the application of information theory approaches to understand movement ecology and unifies insights from existing biomechanical, cognitive, random and optimality movement paradigms. At the cost of requiring ants to obtain (noisy) resource gradient information, we show that this model is both efficient and matches a number of characteristics of real ant exploration. Optimal foraging and the information theory of gambling Roland J. Baddeley , Nigel R. Franks and Edmund R. Hunt JRS Interface Source: royalsocietypublishing.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3De07eca9aab-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=Tn4i1t_2APG2TlaWDq63DBYxdZ0ZbIqGKYFBYKWpJVc&s=m1DtJTjMcolb69pO5kOkMu2ssG6xdUME82LWnnfWKlY&e= ) Data-driven strategies for optimal bicycle network growth Urban transportation networks, from sidewalks and bicycle paths to streets and rail lines, provide the backbone for movement and socioeconomic life in cities. These networks can be understood as layers of a larger multiplex transport network. Because most cities are car-centric, the most developed layer is typically the street layer, while other layers can be highly disconnected. To make urban transport sustainable, cities are increasingly investing to develop their bicycle networks. However, given the usually patchy nature of the bicycle network layer, it is yet unclear how to extend it comprehensively and effectively given a limited budget. Here we develop data-driven, algorithmic network growth strategies and apply them to cities around the world, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks. We motivate the development of our algorithms with a network component analysis and with multimodal urban fingerprints that reveal different classes of cities depending on the connectedness between different network layers. We introduce two greedy algorithms to add the most critical missing links in the bicycle layer: The first algorithm connects the two largest connected components, the second algorithm connects the largest with the closest component. We show that these algorithms outmatch both a random approach and a baseline minimum investment strategy that connects the closest components ignoring size. Our computational approach outlines novel pathways from car-centric towards sustainable cities by taking advantage of urban data available on a city-wide scale. It is a first step towards a quantitative consolidation of bicycle infrastructure development that can become valuable for urban planners and stakeholders. Data-driven strategies for optimal bicycle network growth Luis Natera, Federico Battiston, Gerardo Iñiguez, Michael Szell Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D729a307b91-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=Tn4i1t_2APG2TlaWDq63DBYxdZ0ZbIqGKYFBYKWpJVc&s=o0_Wg8-sexFJ062gFS16hYZpylJtkcQZdWNdICq4ZEo&e= ) ============================================== Sponsored by the Complex Systems Society. Founding Editor: Gottfried Mayer. Editor-in-Chief: Carlos Gershenson. 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