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SOCNET  April 2017

SOCNET April 2017

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 Apr 2017 09:29:45 -0400

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (251 lines)

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

usual terms apply. YMMV

   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
  _______________________________________________________________________
   NetLab Network                 FRSC                      INSNA Founder
   http://www.chass.utoronto.ca/~wellman           twitter: @barrywellman
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
                        http://amzn.to/zXZg39
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 3 Apr 2017 11:03:35 +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 http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=c26888e266&e=55e25a0e3e



Disease Localization in Multilayer Networks

    http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=e16ca31559&e=55e25a0e3e

We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptible-infected-recovered dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasistationary state method, we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. At variance with what is observed in single-layer networks, we show that disease localization takes place on the layers and not on the nodes of a given layer. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the
supra-contact tensor as a function of the ratio of two spreading rates: If the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we report on an interesting phenomenon, the barrier effect; i.e., for a three-layer configuration, when the layer with the lowest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems, opening new possibilities for the study of spreading processes.


Disease Localization in Multilayer Networks
Guilherme Ferraz de Arruda, Emanuele Cozzo, Tiago P. Peixoto, Francisco A. Rodrigues, and Yamir Moreno
Phys. Rev. X 7, 011014

Source: journals.aps.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=3840670a18&e=55e25a0e3e)



Redundant Interdependencies Boost the Robustness of Multiplex Networks

    http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ad75572378&e=55e25a0e3e

In the analysis of the robustness of multiplex networks, it is commonly assumed that a node is functioning only if its interdependent nodes are simultaneously functioning. According to this model, a multiplex network becomes more and more fragile as the number of layers increases. In this respect, the addition of a new layer of interdependent nodes to a preexisting multiplex network will never improve its robustness. Whereas such a model seems appropriate to understand the effect of interdependencies in the simplest scenario of a network composed of only two layers, it may seem unsuitable to characterize the robustness of real systems formed by multiple network layers. In fact, it seems unrealistic that a real system evolved, through the development of multiple layers of interactions, towards a fragile structure. In this paper, we introduce a model of percolation where the condition that makes a node functional is that the node is functioning in at least two of the layers of
the network. The model reduces to the commonly adopted percolation model for multiplex networks when the number of layers equals two. For larger numbers of layers, however, the model describes a scenario where the addition of new layers boosts the robustness of the system by creating redundant interdependencies among layers. We prove this fact thanks to the development of a message-passing theory that is able to characterize the model in both synthetic and real-world multiplex graphs.


Redundant Interdependencies Boost the Robustness of Multiplex Networks
Filippo Radicchi and Ginestra Bianconi
Phys. Rev. X 7, 011013

Source: journals.aps.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=01f649773f&e=55e25a0e3e)



NetSci 2017 conference details

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=56fa179983&e=55e25a0e3e

NETSCI 2017 REGISTRATION OPEN: http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=fbe03a2203&e=55e25a0e3e

Note: Early-bird discount deadline is May 4

International School and Conference on Network Science

June 19-23, Indianapolis, IN (JW Marriott Indianapolis)  |  http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=2c8d133312&e=55e25a0e3e (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=04c315b94f&e=55e25a0e3e)


SCHOOL: http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=0e95264ba1&e=55e25a0e3e

Day One (Monday, June 19)
Network Structure: Alex Arenas (Universitat Rovira i Virgili)
Contagion and spreading processes on networks: Alessandro Vespignani (Northeastern U)
Day Two (Tuesday, June 20)
Maximum-entropy methods for financial and economic networks: Diego Garlaschelli (Leiden U & Oxford U)
Network Control: Raissa D'Souza (UC Davis)
Learning, Mining, and Networks: Tina Eliassi-Rad (Northeastern U)


SATELLITE SYMPOSIA (June 19 & 20)

Satellite Symposia are full and half-day meetings devoted to particular topics. See full schedule and programs in development: http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=2890e47e3d&e=55e25a0e3e
1. Controlling Complex Networks: From Biological to Social and Technological Systems
2. 2nd Workshop on Statistical Physics of Financial and Economic Networks
3. 1st Annual Consortium for the Society of Young Network Scientists (SYNS)
4. Cognitive Network Science
5. Higher-Order Models in Network Science (HONS 2017)
6. Dynamics on and of Complex Networks ˙˙ X
7. Information, Self-Organizing Dynamics and Synchronization on Networks (ISODS III)
8. Machine Learning in Network Science
9. Network Neuroscience
10. Network Medicine: Quantitative interactome and multilayer networks taking medicine beyond the genome
11. Networks Of Networks: Systemic Risk and Infrastructural Interdependencies (NetONets2017)
12. NetSciReg'17 ˙˙ Network Models in Cellular Regulation
13. Quantifying Success
14. Social Influence in Networks
15. Statistical Inference for Network Models
16. Urban Systems and Networks Science
17. NetCrime˙˙Structure and Mobility of Crime
18. Contagion on Networks: Progress and Issues with Models and Data
19. Network Science for National Defense
20. Strengthening Reproducibility in Network Science
21. NetSciEd 6: Satellite Symposium on Network Science and Education
22. Knowledge Networks in Science and Technology


MAIN CONFERENCE PROGRAM (June 21, 22 & 23)

KEYNOTES AND INVITED SPEAKERS CONFIRMED: http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=da0ed3cbb2&e=55e25a0e3e

Keynote Speakers:

Danielle S. Bassett  (U Penn)

Steve Borgatti (U Kentucky)

Jennifer A. Dunne (Sante Fe Institute)


Invited Speakers:

Meeyoung Cha (KAIST)

Alex Fornito (Monash U)

Lise Getoor (UC Santa Cruz)

César A. Hidalgo (MIT)

Shawndra Hill (Microsoft Research NYC & U Penn)

Maximilian Schich (UT Dallas Arts & Technology)
1. Ángeles Serrano, (U Barcelona)

Roberta Sinatra (Central European U)

Xiaofan Wang (Shanghai Jiao Tong U)


Presentations: The list of abstracts accepted for oral presentations, lightning talks, and posters is available:http://netsci2017.net/abstracts


ABOUT NETSCI 2017

NetSci 2017, the International School and Conference on Network Science, will be held in Indianapolis, Indiana from June 19 to 23, 2017. NetSci 2017 aims to bring together leading researchers and practitioners working in the emerging area of network science. The conference fosters interdisciplinary communication and collaboration in network science research across computer and information sciences, physics, mathematics, statistics, the life sciences, neuroscience, environmental sciences, social sciences, finance and business, arts and design.


ORGANIZERS

PROGRAM COMMITTEE CO-CHAIRS: Yong-Yeol Ahn (Indiana University Bloomington), Ciro Cattuto (ISI Foundation) & Tina Eliassi-Rad (Northeastern University)

SATELLITE CO-CHAIRS: Réka Albert (Pennsylvania State University) & Filippo Radicchi (Indiana University Bloomington)

SCHOOL CO-CHAIRS: Santo Fortunato (Indiana University Bloomington) & M. Ángeles Serrano (Universitat de Barcelona)

SPONSORSHIP CO-CHAIRS: Giovanni Ciampaglia & Alessandro Flammini (Indiana University Bloomington)

GENERAL CO-CHAIRS: Fil Menczer & Olaf Sporns (Indiana University Bloomington)


NetSci 2017 is hosted by the Indiana University Network Science Institute (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=6234950757&e=55e25a0e3e (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=64cde0ea7a&e=55e25a0e3e) ). It is the annual meeting of the Network Science Society (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5b89714b02&e=55e25a0e3e (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=9708d5b279&e=55e25a0e3e) ).

Questions? Email [log in to unmask] (mailto:[log in to unmask]) andhttp://netsci2017.net (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5950877bbf&e=55e25a0e3e)

Follow us on Twitter and Facebook: @NetSci2017

Source: netsci2017.net (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=e4a41959b8&e=55e25a0e3e)



Opportunities and Challenges of Trip Generation Data Collection Techniques Using Cellular Networks

    We are witnessing how urban areas are reclaiming road space, before devoted exclusively to cars, for pedestrians. With the increase of pedestrian activity, we need to update our existing transportation forecasting models by focusing more on people walking. The first step of extending the current models is to start with collecting information on pedestrians needed for the trip generation phase. This article discusses opportunities and limitations of tracking pedestrian activity by utilizing information provided by cellular networks. In order to track people, regardless of the underlying wireless media, two qualifications must be met: first, unique and anonymous identification, and second, geospatial visibility through time. While the latter requirement can be achieved with techniques that are similar for different wireless media, how to uniquely identify a pedestrian using a cellular network is domain-specific. We show that tracking of pedestrians using cellular networks can
be done not only without their constant active participation, but also without disrupting normal cellular service. However, although this method is technically feasible, one should be very careful when wanting to implement it by keeping in mind a very important thing: how to protect people's privacy.


Opportunities and Challenges of Trip Generation Data Collection Techniques Using Cellular Networks

Iva Bojic ; Yuji Yoshimura ; Carlo Ratti

IEEE Communications Magazine > Volume: 55 Issue: 3

Source: ieeexplore.ieee.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=939ac084d0&e=55e25a0e3e)



A ˙˙Digital Alchemist˙˙ Unravels the Mysteries of Complexity

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=8adc3a22b1&e=55e25a0e3e

Computational physicist Sharon Glotzer is uncovering the rules by which complex collective phenomena emerge from simple building blocks.

Source: www.wired.com (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5cb7ec712a&e=55e25a0e3e)



Emergence of communities and diversity in social networks

    Understanding how communities emerge is a fundamental problem in social 
and economic systems. Here, we experimentally explore the emergence of 
communities in social networks, using the ultimatum game as a paradigm for 
capturing individual interactions. We find the emergence of diverse 
communities in static networks is the result of the local interaction 
between responders with inherent heterogeneity and rational proposers in 
which the former act as community leaders. In contrast, communities do not 
arise in populations with random interactions, suggesting that a static 
structure stabilizes local communities and social diversity. Our 
experimental findings deepen our understanding of self-organized 
communities and of the establishment of social norms associated with game 
dynamics in social networks.

Source: www.pnas.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=2a029a1325&e=55e25a0e3e)



Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models

    Sensors, coupled with transceivers, have quickly evolved from technologies purely confined to laboratory test beds to workable solutions used across the globe. These mobile and connected devices form the nuts and bolts required to fulfill the vision of the so-called internet of things (IoT). This idea has evolved as a result of proliferation of electronic gadgets fitted with sensors and often being uniquely identifiable (possible with technological solutions such as the use of Radio Frequency Identifiers). While there is a growing need for comprehensive modeling paradigms as well as example case studies for the IoT, currently there is no standard methodology available for modeling such real-world complex IoT-based scenarios. Here, using a combination of complex networks-based and agent-based modeling approaches, ˙˙we present a novel approach to modeling the IoT. Specifically, the proposed approach uses the Cognitive Agent-Based Computing (CABC) framework to simulate complex
IoT networks. We demonstrate modeling of several standard complex network topologies such as lattice, random, small-world, and scale-free networks. To further demonstrate the effectiveness of the proposed approach, we also present a case study and a novel algorithm for autonomous monitoring of power consumption in networked IoT devices. We also discuss and compare the presented approach with previous approaches to modeling. Extensive simulation experiments using several network configurations demonstrate the effectiveness and viability of the proposed approach.


Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models
Komal Batool and Muaz A. Niazi
Complex Adaptive Systems Modeling 2017 5:4
DOI: 10.1186/s40294-017-0043-1

Source: casmodeling.springeropen.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=25774a914e&e=55e25a0e3e)


==============================================
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 (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=8066fe6c0a&e=55e25a0e3e ) and using the "Suggest" button.
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