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

Even while we are in New Orleans recovering of 3 days of standing for the 
French Quarter Music Fest, I think networks.

   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
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   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 16 Apr 2018 11:03:23 +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-3D528ff9f119-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=ZZldVdzITS8wwBMzBzFtkncPVUkO9r7BDFGmbRSiMH8&s=P9tj9wWShv5zGiXcrIeKxr4dK51s6I80Lou-2YjNldI&e=



Multilayer Networks in a Nutshell

    Complex systems are characterized by many interacting units that give 
rise to emergent behavior. A particularly advantageous way to study these 
systems is through the analysis of the networks that encode the 
interactions among the system's constituents. During the last two decades, 
network science has provided many insights in natural, social, biological 
and technological systems. However, real systems are more often than not 
interconnected, with many interdependencies that are not properly captured 
by single layer networks. To account for this source of complexity, a more 
general framework, in which different networks evolve or interact with 
each other, is needed. These are known as multilayer networks. Here we 
provide an overview of the basic methodology used to describe multilayer 
systems as well as of some representative dynamical processes that take 
place on top of them. We round off the review with a summary of several 
applications in diverse fields of science.


Multilayer Networks in a Nutshell
Alberto Aleta, Yamir Moreno

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D9f9485f859-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=ZZldVdzITS8wwBMzBzFtkncPVUkO9r7BDFGmbRSiMH8&s=tsQXhsHNNXA-3sgtN8Et22jzQladrwzBKX9vQ4Cna7o&e=)



Bursty Human Dynamics

    Bursty dynamics is a common temporal property of various complex systems in Nature but it also characterises the dynamics of human actions and interactions. At the phenomenological level it is a feature of all systems that evolve heterogeneously over time by alternating between periods of low and high event frequencies. In such systems, bursts are identified as periods in which the events occur with a rapid pace within a short time-interval while these periods are separated by long periods of time with low frequency of events. As such dynamical patterns occur in a wide range of natural phenomena, their observation, characterisation, and modelling have been a long standing challenge in several fields of research. However, due to some recent developments in communication and data collection techniques it has become possible to follow digital traces of actions and interactions of humans from the individual up to the societal level. This led to several new observations of bursty
phenomena in the new but largely unexplored area of human dynamics, which called for the renaissance to study these systems using research concepts and methodologies, including data analytics and modelling. As a result, a large amount of new insight and knowledge as well as innovations have been accumulated in the field, which provided us a timely opportunity to write this brief monograph to make an up-to-date review and summary of the observations, appropriate measures, modelling, and applications of heterogeneous bursty patterns occurring in the dynamics of human behaviour.


Bursty Human Dynamics
Márton Karsai, Hang-Hyun Jo, Kimmo Kaski

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Da4020aaabc-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=ZZldVdzITS8wwBMzBzFtkncPVUkO9r7BDFGmbRSiMH8&s=YGiXNnb77GMApI_lQLoj-2bgrBACRymSmu8QSNhhL2o&e=)



Sixteenth-Century Pharmacology and the Controversy between Reductionism and Emergentism

    Although in the sixteenth century some pharmacological powers were widely ascribed to celestial influences, alternative views of the nature of such powers began to be developed: Reductionism, according to which all pharmacological powers could be understood as combinations of the powers of elementary qualities, and emergentism, according to which some pharmacological powers are irreducible to combinations of the powers of elementary but arise out of their combination and interaction. The former view can be traced in the work of Francisco Valles (1524˙˙1592) and Thomas Erastus (1524˙˙1583), the latter view in the work of Girolamo Mercuriale (1530˙˙1606) and Jacob Schegk (1511˙˙1587).


Sixteenth-Century Pharmacology and the Controversy between Reductionism and Emergentism

Andreas Blank
Perspectives on Science
Volume 26 | Issue 2 | March-April 2018
p.157-184

https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D908c145040-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=ZZldVdzITS8wwBMzBzFtkncPVUkO9r7BDFGmbRSiMH8&s=vsJJzbACHw_Ve_r11q3r1Rh08_0zVtdm6G5lVY_UQ5s&e=

Source: www.mitpressjournals.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D6ca69f4a52-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=ZZldVdzITS8wwBMzBzFtkncPVUkO9r7BDFGmbRSiMH8&s=jhEsC73DCKxDRP5EUTxShtDDtUruOHoMqnuhg7iplTc&e=)



Seeking Open-Ended Evolution in Swarm Chemistry II: Analyzing Long-Term Dynamics via Automated Object Harvesting

    We studied the long-term dynamics of evolutionary Swarm Chemistry by extending the simulation length ten-fold compared to earlier work and by developing and using a new automated object harvesting method. Both macroscopic dynamics and microscopic object features were characterized and tracked using several measures. Results showed that the evolutionary dynamics tended to settle down into a stable state after the initial transient period, and that the extent of environmental perturbations also affected the evolutionary trends substantially. In the meantime, the automated harvesting method successfully produced a huge collection of spontaneously evolved objects, revealing the system's autonomous creativity at an unprecedented scale.


Seeking Open-Ended Evolution in Swarm Chemistry II: Analyzing Long-Term Dynamics via Automated Object Harvesting
Hiroki Sayama

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D217a48e829-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=ZZldVdzITS8wwBMzBzFtkncPVUkO9r7BDFGmbRSiMH8&s=kgGALKg5ndHugnn1O_4w60RaC2RYvwNbreuI7Uk5JhA&e=)


The Dynamics of Interacting Swarms

    Swarms are self-organized dynamical coupled agents which evolve from simple rules of communication. They are ubiquitous in nature, and be- coming more prominent in defense applications. Here we report on a preliminary study of swarm collisions for a swarm model in which each agent is self-propelling but globally communicates with other agents. We generalize previous models by investigating the interacting dynamics when delay is introduced to the communicating agents. One of our major find- ings is that interacting swarms are far less likely to flock cohesively if they are coupled with delay. In addition, parameter ranges based on coupling strength, incidence angle of collision, and delay change dramatically for other swarm interactions which result in flocking, milling, and scattering.


The Dynamics of Interacting Swarms
Carl Kolon, Ira B. Schwartz

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D3fe046bcd0-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=ZZldVdzITS8wwBMzBzFtkncPVUkO9r7BDFGmbRSiMH8&s=wUaenMQVri9-TXJ9oIDlJ19GQMzE8dN0UWtSxn_bgV8&e=)



Spatial diffusion and churn of social media

    Innovative ideas, products or services spread on social networks that, in the digital age, are maintained to large extent via telecommunication tools such as emails or social media. One of the intriguing puzzles in social contagion under such conditions is the role of physical space. It is not understood either how geography influences the disappearance of products at the end of their life-cycle. In this paper, we utilize a unique dataset compiled from a Hungarian on-line social network (OSN) to uncover novel features in the spatial adoption and churn of digital technologies. The studied OSN was established in 2002 and failed in international competition about a decade later. We find that early adopter towns churn early; while individuals tend to follow the churn of nearby friends and are less influenced by the churn of distant contacts. An agent-based Bass Diffusion Model describes the process how the product gets adopted in the overall population. We show the limitations of
the model regarding the spatial aspects of diffusion and identify the directions of model corrections. Assortativity of adoption time, urban scaling of adoption over the product life-cycle and a distance decay function of diffusion probability are the main factors that spatial diffusion models need to account for.


Spatial diffusion and churn of social media
Balázs Lengyel, Riccardo Di Clemente, János Kertész, Marta C. González

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D7bcdfcab54-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=ZZldVdzITS8wwBMzBzFtkncPVUkO9r7BDFGmbRSiMH8&s=RYG7J3AqiJHMwl4fZMwFzkBXLDayhrU_WbmW1NZa8QE&e=)



CITIZEN DATA SCIENCE FOR SOCIAL GOOD IN COMPLEX SYSTEMS

    The confluence of massive amounts of openly available data, sophisticated machine learning algorithms and an enlightened citizenry willing to engage in data science presents novel opportunities for crowd sourced data science for social good. In this submission, I present vignettes of data science projects that I have been involved in and which have impact in various spheres of life and on social good. Complex systems are all around us: from social networks to transportation systems, cities, economies and financial markets. Understanding these complex systems may lead to solutions for problems ranging from famines, global crises, poverty, climate change and sustainable living despite over-population. Big data and citizen data science allows unprecedented computational power and collective intelligence to be brought to bear on fundamental challenges facing humanity like poverty, diseases, famines and developmental challenges.


CITIZEN DATA SCIENCE FOR
SOCIAL GOOD IN COMPLEX SYSTEMS

Soumya Banerjee
INDECS 16(1), 88-91, 2018
DOI 10.7906/indecs.16.1.6

Source: indecs.eu (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Da2a98f4421-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=ZZldVdzITS8wwBMzBzFtkncPVUkO9r7BDFGmbRSiMH8&s=seSU_VIEYZsxtx5cBIcg8WbP95TlaFPGV-QYPfp6Cy0&e=)



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Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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