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SOCNET  October 2019

SOCNET October 2019

Subject:

Selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 28 Oct 2019 09:52:26 -0400

Content-Type:

MULTIPART/MIXED

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TEXT/PLAIN (133 lines)

*****  To join INSNA, visit http://www.insna.org  *****



   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=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=Zn05Y5OfQNnZmmG9cQYfn6kAVqcUOdh_U17Yz2gE4nY&e=             https://urldefense.proofpoint.com/v2/url?u=http-3A__amzn.to_zXZg39&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=i8LXwv1ljXdR8S9bPVDug7_OlthxGrvB6F8XOX6SFCw&e= 
              https://urldefense.proofpoint.com/v2/url?u=https-3A__en.wikipedia.org_wiki_Barry-5FWellman&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=_QjKTj0oFwlKvueFA_g4U7AXbSAq5NFUDCanmIJcKVw&e= 
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 28 Oct 2019 12:01:05 +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-3D8c183deedf-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=xK_X-Nz0m3QValOWcx4yMDXKg0N-Z8rdlvBg1o5ioXw&e= 


A Power Law Keeps the Brain˙˙s Perceptions Balanced

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dec8cb6a00f-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=H_Twaeym0pQFETXGKLJoIbBxibwC8Nq0TL3J9Fa8GJw&e= 

Researchers have discovered a surprising mathematical relationship in the brain˙˙s representations of sensory information, with possible applications to AI research.

Source: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.quantamagazine.org&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=UOA_D50u2hLb0gSTol4XU6HK0Ty6j1pJrXllXVTCycQ&e=  (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Deefd682590-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=idBs6BeSya4EshbNuVez6mGIoWEnVudIogBZquvRkjQ&e= )



Systematic comparison between methods for the detection of influential spreaders in complex networks

    Influence maximization is the problem of finding the set of nodes of a network that maximizes the size of the outbreak of a spreading process occurring on the network. Solutions to this problem are important for strategic decisions in marketing and political campaigns. The typical setting consists in the identification of small sets of initial spreaders in very large networks. This setting makes the optimization problem computationally infeasible for standard greedy optimization algorithms that account simultaneously for information about network topology and spreading dynamics, leaving space only to heuristic methods based on the drastic approximation of relying on the geometry of the network alone. The literature on the subject is plenty of purely topological methods for the identification of influential spreaders in networks. However, it is unclear how far these methods are from being optimal. Here, we perform a systematic test of the performance of a multitude of heuristic methods for
the identification of influential spreaders. We quantify the performance of the various methods on a corpus of 100 real-world networks; the corpus consists of networks small enough for the application of greedy optimization so that results from this algorithm are used as the baseline needed for the analysis of the performance of the other methods on the same corpus of networks. We find that relatively simple network metrics, such as adaptive degree or closeness centralities, are able to achieve performances very close to the baseline value, thus providing good support for the use of these metrics in large-scale problem settings. Also, we show that a further 2˙˙5% improvement towards the baseline performance is achievable by hybrid algorithms that combine two or more topological metrics together. This final result is validated on a small collection of large graphs where greedy optimization is not applicable.


Systematic comparison between methods for the detection of influential spreaders in complex networks
˙˙irag Erkol, Claudio Castellano & Filippo Radicchi
Scientific Reports volume 9, Article number: 15095 (2019)

Source: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.nature.com&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=QV4SYv_PRkrjtyMGi4CwD8LGoEHr05WjzGlPutbP18w&e=  (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dace7ad9917-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=mtH2gR-ht6lw5DgMabr5s6j8n0ketKGHbK5DsIaRIJ4&e= )


Segregation and polarization in urban areas

    Social behaviours emerge from the exchange of information among 
individuals˙˙constrained by and reciprocally influencing the structure of 
information flows. The Internet radically transformed communication by 
democratizing broadcast capabilities and enabling easy and borderless 
formation of new acquaintances. However, actual information flows are 
heterogeneous and confined to self-organized echo-chambers. Of central 
importance to the future of society is understanding how existing physical 
segregation affects online social fragmentation. Here, we show that the 
virtual space is a reflection of the geographical space where physical 
interactions and proximity-based social learning are the main transmitters 
of ideas. We show that online interactions are segregated by income just 
as physical interactions are, and that physical separation reflects 
polarized behaviours beyond culture or politics. Our analysis is 
consistent with theoretical concepts suggesting polarization is associated 
with social exposure that reinforces within-group homogenization and 
between-group differentiation, and they together promote social 
fragmentation in mirrored physical and virtual spaces.


Segregation and polarization in urban areas
Alfredo J. Morales, Xiaowen Dong, Yaneer Bar-Yam and Alex ˙˙Sandy˙˙ Pentland

Royal Society Open Science

Source: royalsocietypublishing.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D005d479241-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=pTklNMW1G1_POOIskWJp30WOgcByllm1mjfTx6QrJUA&e= )


Large scale and information effects on cooperation in public good games

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dcf599a056c-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=ZK_yjZzWuC2WsKaZoBhWwA9UbA6WCmllZbnY-Uj1DCU&e= 

The problem of public good provision is central in economics and touches 
upon many challenging societal issues, ranging from climate change 
mitigation to vaccination schemes. However, results which are supposed to 
be applied to a societal scale have only been obtained with small groups 
of people, with a maximum group size of 100 being reported in the 
literature. This work takes this research to a new level by carrying out 
and analysing experiments on public good games with up to 1000 
simultaneous players. The experiments are carried out via an online 
protocol involving daily decisions for extended periods. Our results show 
that within those limits, participants˙˙ behaviour and collective outcomes 
in very large groups are qualitatively like those in smaller ones. On the 
other hand, large groups imply the difficulty of conveying information on 
others˙˙ choices to the participants. We thus consider different 
information conditions and show that they have a drastic effect on 
subjects˙˙ contributions. We also classify the individual decisions and 
find that they can be described by a moderate number of types. Our 
findings allow to extend the conclusions of smaller experiments to larger 
settings and are therefore a relevant step forward towards the 
understanding of human behaviour and the organisation of our society.


Large scale and information effects on cooperation in public good games
María Pereda, Ignacio Tamarit, Alberto Antonioni, Jose A. Cuesta, Penélope Hernández & Angel Sánchez
Scientific Reports volume 9, Article number: 15023 (2019)

Source: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.nature.com&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=QV4SYv_PRkrjtyMGi4CwD8LGoEHr05WjzGlPutbP18w&e=  (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D41c3486b2a-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=YQpN5ShQmn4-sQ5U3FUsCdKwcQDLQhWjgedP5a7vsBM&e= )


==============================================
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 (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Ddaa0a1620b-26e-3D55e25a0e3e&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-Kr9B264qKKzDY1OFNxkhX82wZ1kqAfp7LaWnKWvKfw&s=W0K---9lMVZqpKCA9F75W4tyJvmBHZ4KYJO8eR64h_U&e=  ) and using the "Suggest" button.
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