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SOCNET  January 2018

SOCNET January 2018

Subject:

Latest Complexity Digest Posts(fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 22 Jan 2018 10:18:29 -0500

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (226 lines)

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

As soon of you have noted, I haven't circulated Complexity Digest 
selectons for well over a month. One Socnetter was even concerned about my 
health. I am just reliant on what ComDig sends to me, and they were 
offline.

But here are my selections from the new one for the new year.

PS: I will be in Tucson for February, if you are in the area.

   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
        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=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=-XaX0mnIpbZ9YIOwN0tapG-wHd3pXeetSkRnILZqLqo&e=            https://urldefense.proofpoint.com/v2/url?u=http-3A__amzn.to_zXZg39&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=ooRATw67CtRq4ry-ajP8vYqraMLDmM9J6ggDflF4zbw&e=
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 22 Jan 2018 12:03:10 +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-3D69fe0390c9-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=PFbkqJ9XAR1_iTK_ohDGC_soh7DsDu5qKxhTugmD9d0&e=



Random walks and diffusion on networks

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D2b3bba5583-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=5vDffVjCEPPeGMYcOlvtexWBozZX8N2TozwdqgNnWyE&e=

Random walks are ubiquitous in the sciences, and they are interesting from 
both theoretical and practical perspectives. They are one of the most 
fundamental types of stochastic processes; can be used to model numerous 
phenomena, including diffusion, interactions, and opinions among humans 
and animals; and can be used to extract information about important 
entities or dense groups of entities in a network. Random walks have been 
studied for many decades on both regular lattices and (especially in the 
last couple of decades) on networks with a variety of structures. In the 
present article, we survey the theory and applications of random walks on 
networks, restricting ourselves to simple cases of single and non-adaptive 
random walkers. We distinguish three main types of random walks: 
discrete-time random walks, node-centric continuous-time random walks, and 
edge-centric continuous-time random walks. We first briefly survey random 
walks on a line, and then we consider random walks on various types of 
networks. We extensively discuss applications of random walks, including 
ranking of nodes (e.g., PageRank), community detection, respondent-driven 
sampling, and opinion models such as voter models.

[BW: Remembering Al Klovdahl's paper on random walks decades ago]


Random walks and diffusion on networks
Naoki Masuda, Mason A. Porter, Renaud Lambiotte

Physics Reports
Volumes 716˙˙717, 22 November 2017, Pages 1-58

Source: www.sciencedirect.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Da55014538c-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=gxUetWPoL9KMbSAVvFAFo0yQzIOUrgW2fDqE1QmSLTQ&e=)



Sensitive Dependence of Optimal Network Dynamics on Network Structure

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Df49d9c135f-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=rmQBUJgnQ5V491NurWBxNf98x1gV6PD7vV4LcXwn-f0&e=

The relationship between the structure and dynamics of a network is key to 
understanding the behavior of complex systems. A new analysis shows how 
network optimization, whether designed or evolved, can lead to collective 
dynamics that depend sensitively on the structure of the network.


Sensitive Dependence of Optimal Network Dynamics on Network Structure

Takashi Nishikawa, Jie Sun, and Adilson E. Motter
Phys. Rev. X 7, 041044

Source: journals.aps.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Df8a434f2e9-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=cdS2gwmMhADC8oZ-y5WYufdMr1z9zd_96GrfYP-wsUk&e=)



Control energy scaling in temporal networks

    In practical terms, controlling a network requires manipulating a large 
number of nodes with a comparatively small number of external inputs, a 
process that is facilitated by paths that broadcast the influence of the 
(directly-controlled) driver nodes to the rest of the network. Recent work 
has shown that surprisingly, temporal networks can enjoy tremendous 
control advantages over their static counterparts despite the fact that in 
temporal networks such paths are seldom instantaneously available. To 
understand the underlying reasons, here we systematically analyze the 
scaling behavior of a key control cost for temporal networks--the control 
energy. We show that the energy costs of controlling temporal networks are 
determined solely by the spectral properties of an "effective" Gramian 
matrix, analogous to the static network case. Surprisingly, we find that 
this scaling is largely dictated by the first and the last network 
snapshot in the temporal sequence, independent of the number of 
intervening snapshots, the initial and final states, and the number of 
driver nodes. Our results uncover the intrinsic laws governing why and 
when temporal networks save considerable control energy over their static 
counterparts.


Control energy scaling in temporal networks
Aming Li, Sean P. Cornelius, Yang-Yu Liu, Long Wang, Albert-László Barabási

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D86e642c789-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=rUv2JvDmwzv5-qavQowELWFmw6OODrxeo3YyZshp__g&e=)



Understanding predictability and exploration in human mobility

    Predictive models for human mobility have important applications in 
many fields including traffic control, ubiquitous computing, and 
contextual advertisement. The predictive performance of models in 
literature varies quite broadly, from over 90% to under 40%. In this work 
we study which underlying factors - in terms of modeling approaches and 
spatio-temporal characteristics of the data sources - have resulted in 
this remarkably broad span of performance reported in the literature. 
Specifically we investigate which factors influence the accuracy of 
next-place prediction, using a high-precision location dataset of more 
than 400 users observed for periods between 3 months and one year. We show 
that it is much easier to achieve high accuracy when predicting the 
time-bin location than when predicting the next place. Moreover, we 
demonstrate how the temporal and spatial resolution of the data have 
strong influence on the accuracy of prediction. Finally we reveal that the 
exploration of new locations is an important factor in human mobility, and 
we measure that on average 20-25% of transitions are to new places, and 
approx. 70% of locations are visited only once. We discuss how these 
mechanisms are important factors limiting our ability to predict human 
mobility.


Understanding predictability and exploration in human mobility
Andrea Cuttone, Sune Lehmann and Marta C. González
EPJ Data Science20187:2
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D03f140e1bc-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=VDIuraEatI0B-0HmR5YsGmvqHaSB-y4Tl4qWuOJB9CI&e=

Source: epjdatascience.springeropen.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D8c29829d11-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=g6TF5VCFR0YL1oQUCQ0WEIIZg0q-VLZxIucjA0wh-X0&e=)


Socioeconomic characterization of regions through the lens of individual financial transactions

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Ddd2b198809-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=j0VipQEGf6yZzw9ouxslqwx2RFbIjGVNzRLb3U_pCcI&e=

People are increasingly leaving digital traces of their daily activities 
through interacting with their digital environment. Among these traces, 
financial transactions are of paramount interest since they provide a 
panoramic view of human life through the lens of purchases, from food and 
clothes to sport and travel. Although many analyses have been done to 
study the individual preferences based on credit card transaction, 
characterizing human behavior at larger scales remains largely unexplored. 
This is mainly due to the lack of models that can relate individual 
transactions to macro-socioeconomic indicators. Building these models, not 
only can we obtain a nearly real-time information about socioeconomic 
characteristics of regions, usually available yearly or quarterly through 
official statistics, but also it can reveal hidden social and economic 
structures that cannot be captured by official indicators. In this paper, 
we aim to elucidate how macro-socioeconomic patterns could be understood 
based on individual financial decisions. To this end, we reveal the 
underlying interconnection of the network of spending leveraging 
anonymized individual credit/debit card transactions data, craft 
micro-socioeconomic indices that consists of various social and economic 
aspects of human life, and propose a machine learning framework to predict 
macro-socioeconomic indicators.


Hashemian B, Massaro E, Bojic I, Murillo Arias J, Sobolevsky S, Ratti C (2017) Socioeconomic characterization of regions through the lens of individual financial transactions. PLoS ONE 12(11): e0187031. https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D20eb1eb924-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=CDm_kthgxwKa0iQD3dUs-Nqt0NF7G_RhJAtD-onNDtg&e=

Source: journals.plos.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D8c6c678931-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=9xDkgvng40ZkwKSbOFqfJ4bTirmIq0o2fS6sWMmmuFY&e=)




Mobility and Congestion in Dynamical Multilayer Networks with Finite Storage Capacity

    Multilayer networks describe well many real interconnected 
communication and transportation systems, ranging from computer networks 
to multimodal mobility infrastructures. Here, we introduce a model in 
which the nodes have a limited capacity of storing and processing the 
agents moving over a multilayer network, and their congestions trigger 
temporary faults which, in turn, dynamically affect the routing of agents 
seeking for uncongested paths. The study of the network performance under 
different layer velocities and node maximum capacities, reveals the 
existence of delicate trade-offs between the number of served agents and 
their time to travel to destination. We provide analytical estimates of 
the optimal buffer size at which the travel time is minimum and of its 
dependence on the velocity and number of links at the different layers. 
Phenomena reminiscent of the Slower Is Faster (SIF) effect and of the 
Braess' paradox are observed in our dynamical multilayer set-up.


Mobility and Congestion in Dynamical Multilayer Networks with Finite Storage Capacity
Sabato Manfredi, Edmondo Di Tucci, Vito Latora

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D114dc621ab-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=EskE5E9eB2A1FvZ3804wqCKXZS_KZDa5ndLPIBYhNPY&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-3D9668a9e9e2-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=UrDsIGW-nEn2vVrtLb6BEh5G08vTjbj210M4CspjZsY&s=bcknh95ygwRjhdk_plYsg9vIR6B4-3xxwz8bepZ5Ccc&e= ) and using the "Suggest" button.
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