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As the entire world celebrates the Toronto Argonauts victory in the 
original Super Bowl (aka Grey Cup)--which is what the weather looks like

   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
    The earth to be spannd, connected by network
                  Walt Whitman
  _______________________________________________________________________
   NetLab Network                 FRSC                      INSNA Founder
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
   Distinguished Senior Advisor     	     University Learning Academy
   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=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=tb7gJQqlrxTdudj12qo7cJhXYAW-aE2ACk60zikYA3I&e=             https://urldefense.proofpoint.com/v2/url?u=http-3A__amzn.to_zXZg39&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=9anenOsQdQITMSQRYwv6HRkYbN--9_4FL0R3GRxme3E&e= 
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---------- Forwarded message ----------
Date: Mon, 27 Nov 2017 12:04:06 +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 https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Decd824aa38-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=SrMEdIOJ85FLryH7RLr3o8_YxgsRmtG0_uZHLmQmxD0&e= 



The fundamental advantages of temporal networks

    Historically, network science focused on static networks, in which nodes are connected by permanent links. However, in networked systems ranging from protein-protein interactions to social networks, links change. Although it might seem that permanent links would make it easier to control a system, Li et al. demonstrate that temporality has advantages in real and simulated networks. Temporal networks can be controlled more efficiently and require less energy than their static counterparts.


The fundamental advantages of temporal networks
A. Li, S. P. Cornelius, Y.-Y. Liu, L. Wang, A.-L. Barabási
Science  24 Nov 2017:
Vol. 358, Issue 6366, pp. 1042-1046
DOI: 10.1126/science.aai7488

Source: science.sciencemag.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D649e113904-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=EzTOMj0Wh2CyJ0QeYEn1fg1l_Nb6RM1sjR59Ggk9g_o&e= )



The architecture of mutualistic networks as an evolutionary spandrel

    Mutualistic networks have been shown to involve complex patterns of interactions among animal and plant species, including a widespread presence of nestedness. The nested structure of these webs seems to be positively correlated with higher diversity and resilience. Moreover, these webs exhibit marked measurable structural patterns, including broad distributions of connectivity, strongly asymmetrical interactions and hierarchical organization. Hierarchical organization is an especially interesting property, since it is positively correlated with biodiversity and network resilience, thus suggesting potential selection processes favouring the observed web organization. However, here we show that all these structural quantitative patterns˙˙and nestedness in particular˙˙can be properly explained by means of a very simple dynamical model of speciation and divergence with no selection-driven coevolution of traits. The agreement between observed and modelled networks suggests that the
patterns displayed by real mutualistic webs might actually represent evolutionary spandrels.


The architecture of mutualistic networks as an evolutionary spandrel
Sergi Valverde, Jordi Piñero, Bernat Corominas-Murtra, Jose Montoya, Lucas Joppa & Ricard Solé
Nature Ecology & Evolution (2017)
doi:10.1038/s41559-017-0383-4

Source: www.nature.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D785339c57b-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=iJA1hgjj-H9xn7YreNOoaLlmTOtuWHRB27Xabbvt6rg&e= )



Social Complex Contagion in Music Listenership: A Natural Experiment with 1.3 Million Participants

    Can live music events generate complex contagion in music streaming? This paper finds evidence in the affirmative, but only for the most popular artists. We generate a novel dataset from Last.fm, a music tracking website, to analyse the listenership history of 1.3 million users over a two-month time horizon. We use daily play counts along with event attendance data to run a regression discontinuity analysis in order to show the causal impact of concert attendance on music listenership among attendees and their friends network. First, we show that attending a music artist's live concert increases that artist's listenership among the attendees of the concert by approximately 1 song per day per attendee (p-value<0.001). Moreover, we show that this effect is contagious and can spread to users who did not attend the event. However, the extent of contagion depends on the type of artist. We only observe contagious increases in listenership for well-established, popular artists (.06
more daily plays per friend of an attendee [p<0.001]), while the effect is absent for emerging stars. We also show that the contagion effect size increases monotonically with the number of friends who have attended the live event.


Social Complex Contagion in Music Listenership: A Natural Experiment with 1.3 Million Participants
John Ternovski, Taha Yasseri

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D04fb766c7a-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=6sA3AhO8bkTmky5QA8noP6k4E7AHV0DZZWx0vLHdN8Y&e= )



Social network and temporal discounting

    For reasons of social influence and social logistics, people in closed networks are expected to experience time compression: The more closed a person's network, the steeper the person's discount function, and the more narrow the expected time horizon within which the person deliberates events and behavior. Consistent with the hypothesis, data on managers at the top of three organizations show network closure associated with a social life compressed into daily contact with colleagues. Further, language in closed networks is predominantly about current activities, ignoring the future. Further still, discount functions employed by executive MBA students show more severe discounting by students in more closed networks. Inattention to the future can be argued to impair achievement, however, I find no evidence across the managers of daily contact diminishing the achievement associated with network advantage. I close with comments on replication and extrapolation to language more
generally, within-person variation, and select cognitive patterns (closure bias, end of history, and felt status loss).



Social network and temporal discounting

RONALD S. BURT
Network Science, Volume 5 / Issue 4, November 2017, pp 411 - 440
doi: 10.1017/nws.2017.23

Source: www.cambridge.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D1f182352a6-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=oki5VtHWwQgV8TK8xSjnPtq49RDeL-31oUg2qwcjZjs&e= )



Analytical framework for the study of epidemic models on activity driven networks

    Network theory has greatly contributed to an improved understanding of epidemic processes, offering an empowering framework for the analysis of real-world data, prediction of disease outbreaks, and formulation of containment strategies. However, the current state of knowledge largely relies on time-invariant networks, which are not adequate to capture several key features of a number of infectious diseases. Activity driven networks (ADNs) constitute a promising modelling framework to describe epidemic spreading over time varying networks, but a number of technical and theoretical gaps remain open. Here, we lay the foundations for a novel theory to model general epidemic spreading processes over time-varying, ADNs. Our theory derives a continuous-time model, based on ordinary differential equations (ODEs), which can reproduce the dynamics of any discrete-time epidemic model evolving over an ADN. A rigorous, formal framework is developed, so that a general epidemic process can
be systematically mapped, at first, on a Markov jump process, and then, in the thermodynamic limit, on a system of ODEs. The obtained ODEs can be integrated to simulate the system dynamics, instead of using computationally intensive Monte Carlo simulations. An array of mathematical tools for the analysis of the proposed model is offered, together with techniques to approximate and predict the dynamics of the epidemic spreading, from its inception to the endemic equilibrium. The theoretical framework is illustrated step-by-step through the analysis of a susceptible˙˙infected˙˙susceptible process. Once the framework is established, applications to more complex epidemic models are presented, along with numerical results that corroborate the validity of our approach. Our framework is expected to find application in the study of a number of critical phenomena, including behavioural changes due to the infection, unconscious spread of the disease by exposed individuals, or the removal
of nodes from the network of contacts.


An analytical framework for the study of epidemic models on activity driven networks
Lorenzo Zino Alessandro Rizzo Maurizio Porfiri
Journal of Complex Networks, cnx056, https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Da7fc1b8cd7-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=cspZXN5k6w2JvgFo9jbB-0yKFC2ckrKtRLNomCrr_kI&e= 

Source: academic.oup.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D3d0e00a6a9-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=ekKAiIbUc4jjt11e7VZuwth2bhyQNaP4rt4mTVm12I0&e= )



Stream Graphs and Link Streams for the Modeling of Interactions over Time

    Graph theory provides a language for studying the structure of relations, and it is often used to study interactions over time too. However, it poorly captures the both temporal and structural nature of interactions, that calls for a dedicated formalism. In this paper, we generalize graph concepts in order to cope with both aspects in a consistent way. We start with elementary concepts like density, clusters, or paths, and derive from them more advanced concepts like cliques, degrees, clustering coefficients, or connected components. We obtain a language to directly deal with interactions over time, similar to the language provided by graphs to deal with relations. This formalism is self-consistent: usual relations between different concepts are preserved. It is also consistent with graph theory: graph concepts are special cases of the ones we introduce. This makes it easy to generalize higher-level objects such as quotient graphs, line graphs, k-cores, and centralities. This
paper also considers discrete versus continuous time assumptions, instantaneous links, and extensions to more complex cases.


Stream Graphs and Link Streams for the Modeling of Interactions over Time
Matthieu Latapy, Tiphaine Viard, Clémence Magnien

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D55826cb7d0-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=UiBWhfZDNIQPbAoSL_ScMYAQCYwr7Q6eoBtWBYJpDgw&e= )



Small vulnerable sets determine large network cascades in power grids

    Sometimes a power failure can be fairly local, but other times, a seemingly identical initial failure can cascade to cause a massive and costly breakdown in the system. Yang et al. built a model for the North American power grid network based on samples of data covering the years 2008 to 2013 (see the Perspective by D'Souza). Although the observed cascades were widespread, a small fraction of all network components, particularly the ones that were most cohesive within the network, were vulnerable to cascading failures. Larger cascades were associated with concurrent triggering events that were geographically closer to each other and closer to the set of vulnerable components.


Small vulnerable sets determine large network cascades in power grids
Yang Yang, Takashi Nishikawa, Adilson E. Motter

Source: science.sciencemag.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dc509bb6823-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=xZb8OKEq1rjDuoex86nJMZVeFA8sbPuZ_nqEvXqPbOo&s=UHU05KnJWzh67ZqPcT4-Xx2zJdJmfKCnb9pYRfUuYiA&e= )


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

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