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SOCNET  May 2018

SOCNET May 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, 14 May 2018 09:31:06 -0400

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***** 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
  _______________________________________________________________________
   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=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=KUojpFbitQs53BrxkZzye-WJfo9u5jnsQe5_Us1whlI&e= https://urldefense.proofpoint.com/v2/url?u=http-3A__amzn.to_zXZg39&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=UmE3c1pqb_w1egmILXxp1JWOW_sWNd678HPUCgZX_ac&e=
              https://urldefense.proofpoint.com/v2/url?u=https-3A__en.wikipedia.org_wiki_Barry-5FWellman&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=xbiKXtAAjWSmSzzqdx2OhNAJjW6rp6cgrW1v54ZDNJE&e=
   _______________________________________________________________________

Academic performance and behavioral patterns

    Identifying the factors that influence academic performance is an
essential part of educational research. Previous studies have documented
the importance of personality traits, class attendance, and social network
structure. Because most of these analyses were based on a single
behavioral aspect and/or small sample sizes, there is currently no
quantification of the interplay of these factors. Here, we study the
academic performance among a cohort of 538 undergraduate students forming
a single, densely connected social network. Our work is based on data
collected using smartphones, which the students used as their primary
phones for two years. The availability of multi-channel data from a single
population allows us to directly compare the explanatory power of
individual and social characteristics. We find that the most informative
indicators of performance are based on social ties and that network
indicators result in better model performance than individual
characteristics (including both personality and class attendance). We
confirm earlier findings that class attendance is the most important
predictor among individual characteristics. Finally, our results suggest
the presence of strong homophily and/or peer effects among university
students.


Academic performance and behavioral patterns

Valentin Kassarnig, Enys Mones, Andreas Bjerre-Nielsen, Piotr Sapiezynski, David Dreyer Lassen and Sune Lehmann
EPJ Data Science 2018 7:10
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D97fe8892ff-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=tFtXN-2pm7S_hy_WH8wi3FH_fMtqM9XCJ8BHERdplw4&e=

Source: epjdatascience.springeropen.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D1edb794970-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=aolFldV1TNGgBHb5z2R6HACbN_S05QQKcQxtO0CbmmI&e=)



The domino effect: an empirical exposition of systemic risk across project networks

    Activity network analysis is a widely used tool for managing project
risk. Traditionally, this type of analysis is used to evaluate task
criticality by assuming linear cause˙˙and˙˙effect phenomena, where the
size of a local failure (e.g. task delay) dictates its possible global
impact (e.g. project delay). Motivated by the question of whether activity
networks are subject to non˙˙linear cause˙˙and˙˙effect phenomena, a
computational framework is developed and applied to real˙˙world project
data to evaluate project systemic risk. Specifically, project systemic
risk is viewed as the result of a cascading process which unravels across
an activity network, where the failure of a single task can consequently
affect its immediate, downstream task(s). As a result, we demonstrate that
local failures are capable of triggering failure cascades of intermittent
sizes. In turn, a modest local disruption can fuel exceedingly large,
systemic failures. In addition, the probability for this to happen is much
higher than anticipated. A systematic examination of why this is the case
is subsequently performed, with results attributing the emergence of
large˙˙scale failures to topological and temporal features of activity
networks. Finally, local mitigation is assessed in terms of containing
these failures cascades ˙˙ results illustrate that this form of mitigation
is both ineffective and insufficient. Given the ubiquity of our findings,
our work has the potential of deepening our current theoretical
understanding on the causal mechanisms responsible for large˙˙scale
project failures.


The domino effect: an empirical exposition of systemic risk across project networks

Christos Ellinas

Production and Operations Management

https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D30559239fe-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=3QzlnVZv8dYhuX8llVxlsfUQtKF34mzY9eukE9iA02U&e=

Source: onlinelibrary.wiley.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dbed71008f0-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=5UK2e_q2ThgEthL5_YISVdOb668qjeFlk7JZR-fa7xw&e=)



Can co-location be used as a proxy for face-to-face contacts?

    Technological advances have led to a strong increase in the number of
data collection efforts aimed at measuring co-presence of individuals at
different spatial resolutions. It is however unclear how much co-presence
data can inform us on actual face-to-face contacts, of particular interest
to study the structure of a population in social groups or for use in
data-driven models of information or epidemic spreading processes. Here,
we address this issue by leveraging data sets containing high resolution
face-to-face contacts as well as a coarser spatial localisation of
individuals, both temporally resolved, in various contexts. The
co-presence and the face-to-face contact temporal networks share a number
of structural and statistical features, but the former is (by definition)
much denser than the latter. We thus consider several down-sampling
methods that generate surrogate contact networks from the co-presence
signal and compare them with the real face-to-face data. We show that
these surrogate networks reproduce some features of the real data but are
only partially able to identify the most central nodes of the face-to-face
network. We then address the issue of using such down-sampled co-presence
data in data-driven simulations of epidemic processes, and in identifying
efficient containment strategies. We show that the performance of the
various sampling methods strongly varies depending on context. We discuss
the consequences of our results with respect to data collection strategies
and methodologies.


Can co-location be used as a proxy for face-to-face contacts?
Mathieu Génois and Alain Barrat
EPJ Data Science 2018 7:11
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D52301a8390-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=7QCrI0r3IpAF4DJWqn4Jy2BZqIJlLp3Fk1lsS9pOrtI&e=

Source: epjdatascience.springeropen.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Ddb264c03a4-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=9T60L_rpeeuyd0R5laWj7ul0nMkIFnAK-kCc-hkBnbI&e=)



Emergent Behavior in Complex Systems Engineering: A Modeling and Simulation Approach

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D3e6a6f358e-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=F3aJrZXxFMAZYbwSPWo5QzbYIV-_6RmN7IacwfzTBEA&e=

A comprehensive text that reviews the methods and technologies that explore emergent behavior in complex systems engineering in multidisciplinary fields

In Emergent Behavior in Complex Systems Engineering, the authors present the theoretical considerations and the tools required to enable the study of emergent behaviors in manmade systems. Information Technology is key to today˙˙s modern world. Scientific theories introduced in the last five decades can now be realized with the latest computational infrastructure. Modeling and simulation, along with Big Data technologies are at the forefront of such exploration and investigation.

The text offers a number of simulation-based methods, technologies, and approaches that are designed to encourage the reader to incorporate simulation technologies to further their understanding of emergent behavior in complex systems. The authors present a resource for those designing, developing, managing, operating, and maintaining systems, including system of systems. The guide is designed to help better detect, analyse, understand, and manage the emergent behaviour inherent in complex systems engineering in order to reap the benefits of innovations and avoid the dangers of unforeseen consequences.


Emergent Behavior in Complex Systems Engineering: A Modeling and Simulation Approach
Saurabh Mittal, Saikou Diallo, Andreas Tolk, William B. Rouse (Series Editor)

Wiley, 2018

Source: www.wiley.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Def927b4a77-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=JMcOYWa7wvoCNbCACBUTp9Yihfw5Lm5Omg-oXR9in2Y&e=)



Logic and connectivity jointly determine criticality in biological gene regulatory networks

    The complex dynamics of gene expression in living cells can be well-approximated using Boolean networks. The average sensitivity is a natural measure of stability in these systems: values below one indicate typically stable dynamics associated with an ordered phase, whereas values above one indicate chaotic dynamics. This yields a theoretically motivated adaptive advantage to being near the critical value of one, at the boundary between order and chaos. Here, we measure average sensitivity for 66 publicly available Boolean network models describing the function of gene regulatory circuits across diverse living processes. We find the average sensitivity values for these networks are clustered around unity, indicating they are near critical. In many types of random networks, mean connectivity and the average activity bias of the logic functions

have been found to be the most important network properties in determining average sensitivity, and by extension a network's criticality. Surprisingly, many of these gene regulatory networks achieve the near-critical state with and

far from that predicted for critical systems: randomized networks sharing the local causal structure and local logic of biological networks better reproduce their critical behavior than controlling for macroscale properties such as and

alone. This suggests the local properties of genes interacting within regulatory networks are selected to collectively be near-critical, and this non-local property of gene regulatory network dynamics cannot be predicted using the density of interactions alone.

Logic and connectivity jointly determine criticality in biological gene regulatory networks
Bryan C. Daniels, Hyunju Kim, Douglas Moore, Siyu Zhou, Harrison Smith, Bradley Karas, Stuart A. Kauffman, Sara I. Walker

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D26b809d5fc-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=A1i7sa-W8S824TjhWByU0UjokT3OMkLKX554Htr5nog&e=)



The evolutions of the rich get richer and the fit get richer phenomena in scholarly networks: the case of the strategic management journal

    Understanding how a scientist develops new scientific collaborations or
how their papers receive new citations is a major challenge in
scientometrics. The approach being proposed simultaneously examines the
growth processes of the co-authorship and citation networks by analyzing
the evolutions of the rich get richer and the fit get richer phenomena. In
particular, the preferential attachment function and author fitnesses,
which govern the two phenomena, are estimated non-parametrically in each
network. The approach is applied to the co-authorship and citation
networks of the flagship journal of the strategic management scientific
community, namely the Strategic Management Journal. The results suggest
that the abovementioned phenomena have been consistently governing both
temporal networks. The average of the attachment exponents in the
co-authorship network is 0.30 while it is 0.29 in the citation network.
This suggests that the rich get richer phenomenon has been weak in both
networks. The right tails of the distributions of author fitness in both
networks are heavy, which imply that the intrinsic scientific quality of
each author has been playing a crucial role in getting new citations and
new co-authorships. Since the total competitiveness in each temporal
network is founded to be rising with time, it is getting harder to receive
a new citation or to develop a new collaboration. Analyzing the average
competency, it was found that on average, while the veterans tend to be
more competent at developing new collaborations, the newcomers are likely
better at acquiring new citations. Furthermore, the author fitness in both
networks has been consistent with the history of the strategic management
scientific community. This suggests that coupling node fitnesses
throughout different networks might be a promising new direction in
analyzing simultaneously multiple networks.


The evolutions of the rich get richer and the fit get richer phenomena in scholarly networks: the case of the strategic management journal

Guillermo Armando Ronda-Pupo, Thong Pham

Scientometrics pp 1˙˙21

Source: link.springer.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D155114afdd-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=GSov8VHmL0kpU3DUOrYxI0ZuZqC34yjDNsiskTqOvhg&s=4wPsRK5gedExLsTzvGSr8TVSgMgRgjkWJSGvdHxaM8Q&e=)

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

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