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SOCNET  December 2018

SOCNET December 2018

Subject:

selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 3 Dec 2018 10:12:58 -0500

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (120 lines)

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

May the 8 days of Hanukah light bring glow to your hearts.
But remember that when you spin the dreidel, the house always wins.

   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
  _______________________________________________________________________
   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=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=LJQ6DMf8JrqcbaQokVN4b8xN_QZQPGxNiSMsBs6VH9o&e=            https://urldefense.proofpoint.com/v2/url?u=http-3A__amzn.to_zXZg39&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=4Mkdje3GgNlGlouRrKIb_GToZXy-NcPu3jt-j24kMN4&e=
              https://urldefense.proofpoint.com/v2/url?u=https-3A__en.wikipedia.org_wiki_Barry-5FWellman&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=qV2bkDGkTCSg82uI3zaO40xALiI0Do1UAfCrnsLOyaU&e=
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 3 Dec 2018 12:05:42 +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-3D980ff3bea9-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=Og8NeCV0G_x31RbCPPcBLNf6yJXkHwJ03dZASx3_Qcw&e=



Statistical physics of liquid brains

    Liquid neural networks (or ''liquid brains'') are a widespread class of cognitive living networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (''solid brains''), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in other ways. On one hand, the attractors found in liquid brains
are not always based on connection weights but instead on population abundances. However, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role of criticality as a way of rapidly reacting to external signals.


Statistical physics of liquid brains
Jordi Pinero, Ricard Sole

Source: www.biorxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Deb02675cb0-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=5Heq1AL99SmURWDnI4r7Hhbb5-w4j85g7xioXTcl8-Q&e=)


Optimal Sequence Memory in Driven Random Networks

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D9ca9ac1cf4-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=d2d6z5DHQwsNfdKmFdAEIs4F9giWQnI6wKXfLeR4FDc&e=

Autonomous, randomly coupled, neural networks display a transition to chaos at a critical coupling strength. Here, we investigate the effect of a time-varying input on the onset of chaos and the resulting consequences for information processing. Dynamic mean-field theory yields the statistics of the activity, the maximum Lyapunov exponent, and the memory capacity of the network. We find an exact condition that determines the transition from stable to chaotic dynamics and the sequential memory capacity in closed form. The input suppresses chaos by a dynamic mechanism, shifting the transition to significantly larger coupling strengths than predicted by local stability analysis. Beyond linear stability, a regime of coexistent locally expansive but nonchaotic dynamics emerges that optimizes the capacity of the network to store sequential input.


Optimal Sequence Memory in Driven Random Networks

Jannis Schuecker, Sven Goedeke, and Moritz Helias
Phys. Rev. X 8, 041029

Source: journals.aps.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D00666d8e79-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=fy2eItykIPKjA5E2HZGyOFYdQUR8eE3p8GFkXkiRU_I&e=)



Quantifying Biases in Online Information Exposure

    Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this article, we mine a massive data set of web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity
bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside ˙˙social bubbles.˙˙


Nikolov, D.; Lalmas, M.; Flammini, A.; and Menczer, F. Journal of the Association for Information Science and Technology. doi:10.1002/asi.24121 - https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D7e8cb880ca-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=EYpOMNn8g0W6JHCmwjo2w4csOQvdRpSVKTMlTAUrrqU&e=

Source: onlinelibrary.wiley.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D8fe2597388-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=lyL2jdqGyDps9ED0oTfbIFxD_0-Ym9h5t25ZSPeL2EI&e=)



Multiscale impact of researcher mobility

    International mobility facilitates the exchange of scientific, institutional and cultural knowledge. Yet whether globalization and advances in virtual communication technologies have altered the impact of researcher mobility is a relevant and open question that we address by analysing a broad international set of 26 170 physicists from 1980 to 2009, focusing on the 10-year period centred around each mobility event to assess the impact of mobility on research outcomes. We account for secular globalization trends by splitting the analysis into three periods, measuring for each period the effect of mobility on researchers' citation impact, research topic diversity, collaboration networks and geographical coordination. In order to identify causal effects we leverage statistical matching methods that pair mobile researchers with non-mobile researchers that are similar in research profile attributes prior the mobility event. We find that mobile researchers gain up to a 17% increase
in citations relative to their non-mobile counterparts, which can be explained by the simultaneous increase in their diversity of co-authors, topics and geographical coordination in the period immediately following migration. Nevertheless, we also observe that researcher's completely curtail prior collaborations with their source country in 11% of the cross-border mobility events. As such, these individual-level perturbations fuel multiscale churning in scientific networks, e.g. rewiring the connectivity of individuals and ideas and affecting international integration. Together these results provide additional clarity on the complex relationship between human capital mobility and the dynamics of social capital investment, with implications for immigration and national innovation system policy.


Multiscale impact of researcher mobility
Alexander M. Petersen

JRS Interface

Source: rsif.royalsocietypublishing.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dd37e64a910-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=3IBfQdm1kSESI7bXcvPUqZvb_wqYzjOykn8G4J40Ay8&e=)




Measurability of the epidemic reproduction number in data-driven contact networks

    The analysis of real epidemiological data has raised issues of the adequacy of the classic homogeneous modeling framework and quantities, such as the basic reproduction number in real-world situations. Based on high-quality sociodemographic data, here we generate a multiplex network describing the contact pattern of the Italian and Dutch populations. By using a microsimulation approach, we show that, for epidemics spreading on realistic contact networks, it is not possible to define a steady exponential growth phase and a basic reproduction number. We show the operational use of the instantaneous reproduction rate as a good descriptor of the transmission dynamics.


Measurability of the epidemic reproduction number in data-driven contact networks
Quan-Hui Liu, Marco Ajelli, Alberto Aleta, Stefano Merler, Yamir Moreno, and Alessandro Vespignani
PNAS

Source: www.pnas.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Df44d70ed63-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=IiWJJPZzk9NMstGuvgWHoqloogZm7RSSsuU-lkluqx0&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-3D5f21a2095e-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2xodnHrtGVvYsSANlYUphMNKZHmbiyM5grLqztBR33w&s=6uH_9OJ_d7u8ur0-pHVxyALMVBomyYyt2zj9WOzp0dM&e= ) and using the "Suggest" button.
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