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Raptors Rapture has hit Toronto, as what may be the final NBA championship 
game is tonight. The city has (temporarily) turned away from ice hockey to 
basketball.

   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--Walter Cronkite
  _______________________________________________________________________
   Director, NetLab Network      			            FRSC
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
         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=pkXez4GjMPfVLfS5WOdER-6naLjj6WYi5FuggJeM34A&s=P6ydlxT2fm-Q9AilhntMBpHqxxgx2UPkQpiaA9PQ8ag&e=             https://urldefense.proofpoint.com/v2/url?u=http-3A__amzn.to_zXZg39&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=pkXez4GjMPfVLfS5WOdER-6naLjj6WYi5FuggJeM34A&s=iAiq3JbNxD4UDz7Ly9Lz6ofsXJe_Ib2N-9mK3kJpQys&e= 
              https://urldefense.proofpoint.com/v2/url?u=https-3A__en.wikipedia.org_wiki_Barry-5FWellman&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=pkXez4GjMPfVLfS5WOdER-6naLjj6WYi5FuggJeM34A&s=gPBrLb7AaWqn74DdcaEiiqJwstRqti_-g7u9SITrD2I&e= 
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 10 Jun 2019 11:01:24 +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-3D6705835372-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=pkXez4GjMPfVLfS5WOdER-6naLjj6WYi5FuggJeM34A&s=p3P8-l3CLm93ronyWhhXLvmjVQxole8BuzDWV0dEtSc&e= 



Human information processing in complex networks

    Humans communicate using systems of interconnected stimuli or concepts -- from language and music to literature and science -- yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient and biased ways that humans process this information. Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system's network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with
high-degree hubs, and clustered, with tightly-connected modules -- the two defining features of hierarchical organization. Together, these results suggest that many real networks are constrained by the pressures of information transmission, and that these pressures select for specific structural features.


Human information processing in complex networks

Christopher W. Lynn, Lia Papadopoulos, Ari E. Kahn, Danielle S. Bassett

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D4ca35715f1-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=pkXez4GjMPfVLfS5WOdER-6naLjj6WYi5FuggJeM34A&s=xl436kmVeAgz5qwI77mr367sVlTzspRhT5USPnSl_I8&e= )



Interacting contagions are indistinguishable from social reinforcement

    From fake news to innovative technologies, many contagions spread via a process of social reinforcement, where multiple exposures are distinct from prolonged exposure to a single source. Contrarily, biological agents such as Ebola or measles are typically thought to spread as simple contagions. Here, we demonstrate that interacting simple contagions are indistinguishable from complex contagions. In the social context, our results highlight the challenge of identifying and quantifying mechanisms, such as social reinforcement, in a world where an innumerable amount of ideas, memes and behaviors interact. In the biological context, this parallel allows the use of complex contagions to effectively quantify the non-trivial interactions of infectious diseases.


Interacting contagions are indistinguishable from social reinforcement

Laurent Hébert-Dufresne, Samuel V. Scarpino, Jean-Gabriel Young

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D377c54fcb3-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=pkXez4GjMPfVLfS5WOdER-6naLjj6WYi5FuggJeM34A&s=eh13-ANYiaDFGG_QeBCvflHIMO1mZmORp2DLdVTMgvA&e= )



Simplicial models of social contagion

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D171039e226-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=pkXez4GjMPfVLfS5WOdER-6naLjj6WYi5FuggJeM34A&s=PVV4U8xjgQTp9E6479Tw2XTTqGr0gwPDjXgTKFwsbn4&e= 

Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterize social contagion processes such as opinion formation or the adoption of novelties, where complex mechanisms of influence and reinforcement are at work. Here we introduce a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes. Numerical simulations of the model on both empirical and synthetic simplicial complexes highlight the emergence of novel phenomena such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding
of higher-order interactions in complex systems.


Simplicial models of social contagion
Iacopo Iacopini, Giovanni Petri, Alain Barrat & Vito Latora
Nature Communications 10, Article number: 2485 (2019)

Source: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.nature.com&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=pkXez4GjMPfVLfS5WOdER-6naLjj6WYi5FuggJeM34A&s=uRH6QOcww7rQjxBjNnRIZGY2kpZvuHAjvuPehIODJuQ&e=  (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dc1aaf11a34-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=pkXez4GjMPfVLfS5WOdER-6naLjj6WYi5FuggJeM34A&s=u6wMQRZoiuuXSS9tdOBiLf0L3wyE5DsLMaJ7R-xSw1I&e= )



Proceedings A Special Feature: A Generation of Network Science. Call for papers

    On the eve of 20th century, three papers launched the modern Network 
Science by bringing it to the attention of a wider community of 
physicists, computer scientists and applied mathematicians. The papers - 
by Watts and Strogatz [1], Barabasi and Albert [2], and Google founders 
Brin and Page [3] - introduced ˙˙small world networks˙˙, ˙˙preferential 
attachment,˙˙ and ˙˙PageRank˙˙ into the vernacular of network scientists. 
They showed that simple models could reproduce much of the complexity 
observed in network structure and that the structure of networks was 
linked to their function. As we mark the 20th anniversary of the 
publication of these seminal works, it is time to reflect on the state of 
Network Science and where the field is headed. What have we learned about 
networks over the past two decades? How does network structure affect its 
function? How do we represent networks, predict and control their 
behavior? How do networks grow and change? What are the limits of our 
understanding, and finally, what are the important open problems in 
network science?

Source: royalsocietypublishing.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D0d3ea90475-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=pkXez4GjMPfVLfS5WOdER-6naLjj6WYi5FuggJeM34A&s=F7jFLkZ3o9PjaBNL5GA029aGdivWzaDjVmY5PX3QRCM&e= )


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Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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