***** To join INSNA, visit http://www.insna.org *****
With fond memories of Lin and Sue Freeman
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
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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
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Date: Mon, 27 Aug 2018 11:05:13 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
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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-3D5efd20b3d5-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=PZ4rtIbQGaMJMgMwJO6gj8zkPWUzveCjo4-iDVze5-Q&e=
Advances on the Resilience of Complex Networks (Complexity Special Issue)
A common property of many complex systems is resilience, that is, the ability of the system to react to perturbations, internal failures, and environmental events by absorbing the disturbance and/or rebuild to maintain its functions. Nowadays, understanding how complex systems demonstrate resilience is critical in many different fields, because examples of collapses and crises caused by low resilience are more and more spreading all over the world including transportation, financial, energy, communication, and ecological systems.
Therefore, in the last decade, the topic of resilience has grown a lot in popularity. Studies on resilience are popular in multiple disciplines, such as ecology, environmental science, computer science, engineering, management science, economics, and phycology. They investigate resilience of a broad variety of complex systems involving individuals, teams, ecosystems, organizations, communities, supply chains, financial networks, computer networks, and building infrastructures.
Despite this multidisciplinary nature, two main perspectives in the conceptualization of resilience are recognized, that is, the static and dynamic ones [1ÿÿ4]. The resilience is static when it focuses on the ability of the system to absorb disturbance and bounce back to the original equilibrium state, maintaining its core functions when shocked. In such a case, the resilience is linked to the ability to recover the original shape and features once stretched (robustness) and the capacity of the system to take alternative positions to respond better to change (flexibility). The dynamic perspective focuses on the ability of the system to evolve over time moving towards a new more favorable equilibrium state. According to this perspective, resilience concerns the adaptive capacity of the system, which is able to react to disturbance by changing its structure, processes, and functions in order to increase its ability to persist [5].
This special issue collects nine papers concerning resilience of complex systems, which accords well with the main features summarized above. They concern studies investigating resilience of complex systems in diverse disciplines (engineering, management science, computer science, economics, and organization science) and adopting both the static and dynamic perspectives. Their aim is to identify the main factors and dynamics influencing resilience of diverse systems (water system infrastructures, organizational teams, financial markets, wireless sensor network, and urban system) to a variety of unexpected and negative events
Complexity
Volume 2018, Article ID 8756418, 3 pages
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D7027485cb5-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=0_8GHej21OBNTbEJsOqS5qsHd9_xvHCFYNuqcw-qNSA&e=
Editorial
Advances on the Resilience of Complex Networks
Ilaria Giannoccaro, Vito Albino, and Anand Nair
Source: www.hindawi.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D344cb43e44-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=Qo0J2gygk2FBCkAVmRk1LuVLx5IroDiBSms8GoJ87hc&e=)
Resilience of Complex Systems: State of the Art and Directions for Future Research
This paper reviews the state of the art on the resilience of complex systems by embracing different research areas and using bibliometric tools. The aim is to identify the main intellectual communities and leading scholars and to synthesize key knowledge of each research area. We also carry out a comparison across the research areas, aimed at analyzing how resilience is approached in any field, how the topic evolved starting from the ecological field of study, and the level of cross-fertilization among domains. Our analysis shows that resilience of complex systems is a multidisciplinary concept, which is particularly important in the fields of environmental science, ecology, and engineering. Areas of recent and increasing interest are also operation research, management science, business, and computer science. Except for environmental science and ecology, research is fragmented and carried out by isolated research groups. Integration is not only limited inside each field
but also between research areas. In particular, we trace the citation links between different research areas and find a very limited number, revealing a scarce cross-fertilization among domains. We conclude by providing some directions for future research.
Complexity
Volume 2018, Article ID 3421529, 44 pages
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D3cb2da3951-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=IjJ6sl941PJm8oLnMwfpql4gW-ixmRY_0FqGjFIWbNw&e=
Review Article
Resilience of Complex Systems: State of the Art and Directions for Future Research
Luca Fraccascia, Ilaria Giannoccaro, and Vito Albino
Source: www.hindawi.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D1266ab65c4-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=23wHljHsks7ANQ3I0frcgFfuBeZZov5YHcAOcqOxHVs&e=)
Sequences of purchases in credit card data reveal lifestyles in urban populations
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dd03212bd6c-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=gPN-fkMPz-RPq84m3x3UzokcYqBY7YRbiUEz-4Fiqt4&e=
Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics, and social sciences. In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchase sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted from their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant
sequences that reveal insights on collective human behavior.
Sequences of purchases in credit card data reveal lifestyles in urban populations
Riccardo Di Clemente, Miguel Luengo-Oroz, Matias Travizano, Sharon Xu, Bapu Vaitla & Marta C. González
Nature Communicationsvolume 9, Article number: 3330 (2018)
Source: www.nature.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dafda5d7640-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=Y3lQC207porJrXvDa7ICUi9eYWa1h5ubg7RdqU6UkD4&e=)
Self-Optimization in Continuous-Time Recurrent Neural Networks
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A recent advance in complex adaptive systems has revealed a new unsupervised learning technique called self-modeling or self-optimization. Basically, a complex network that can form an associative memory of the state configurations of the attractors on which it converges will optimize its structure: it will spontaneously generalize over these typically suboptimal attractors and thereby also reinforce more optimal attractorsÿÿeven if these better solutions are normally so hard to find that they have never been previously visited. Ideally, after sufficient self-optimization the most optimal attractor dominates the state space, and the network will converge on it from any initial condition. This technique has been applied to social networks, gene regulatory networks, and neural networks, but its application to less restricted neural controllers, as typically used in evolutionary robotics, has not yet been attempted. Here we show for the first time that the self-optimization
process can be implemented in a continuous-time recurrent neural network with asymmetrical connections. We discuss several open challenges that must still be addressed before this technique could be applied in actual robotic scenarios.
Self-Optimization in Continuous-Time Recurrent Neural Networks
Mario Zarco and Tom Froese
Front. Robot. AI, 21 August 2018 | https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D60db0b3142-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=KgnYWwkhcEcd_40lETydiIk3iRHS4zRs-OQNfCzxi6M&e=
Source: www.frontiersin.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D50ed660ad4-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=0vZZ1pD1BoChDD34jK9Md8gGtXRTpfxpl_NFLC0mm9U&e=)
An information-theoretic approach to self-organisation: Emergence of complex interdependencies in coupled dynamical systems
Self-organisation lies at the core of fundamental but still unresolved scientific questions, and holds the promise of de-centralised paradigms crucial for future technological developments. While self-organising processes have been traditionally explained by the tendency of dynamical systems to evolve towards specific configurations, or attractors, we see self-organisation as a consequence of the interdependencies that those attractors induce. Building on this intuition, in this work we develop a theoretical framework for understanding and quantifying self-organisation based on coupled dynamical systems and multivariate information theory. We propose a metric of global structural strength that identifies when self-organisation appears, and a multi-layered decomposition that explains the emergent structure in terms of redundant and synergistic interdependencies. We illustrate our framework on elementary cellular automata, showing how it can detect and characterise the
emergence of complex structures.
An information-theoretic approach to self-organisation: Emergence of complex interdependencies in coupled dynamical systems
Fernando Rosas, Pedro A.M. Mediano, Martin Ugarte, Henrik J. Jensen
Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Db396698e41-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=232twz5muqJS3ZWXL7XBWrMGrcQyKyvNcDJIHXU_BtE&e=)
Complexity and Resilience in the Social and Ecological Sciences
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D3a5771da6a-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=ZK61TkY1sVCLs--ijJOBCe9OVg9vBm_jFkvxqXtsmhk&e=
This book introduces a new approach to environmental sociology, by integrating complexity-informed social science, Marxian ecological theory, and resilience-based human ecology. It argues that sociologists have largely ignored developments in ecology which move beyond functionalist approaches to systems analysis, and as a result, environmental sociology has failed to capitalise not only on the analytical promise of resilience ecology, but on complementary developments in complexity theory. By tracing the origins and discussing current developments in each of these areas, it offers several paths to interdisciplinary dialogue. Eoin Flaherty argues that complexity theory and Marxian ecology can enhance our understanding of the social aspect of social-ecological systems, whilst a resilience approach can sharpen the analytical power of environmental sociology.
Complexity and Resilience in the Social and Ecological Sciences
Eoin Flaherty
Springer
Source: link.springer.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D9bb3d2833b-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=iyYpjYz3WZre2urM9xenHV8iPOnZ2TDwLWsiCnuMG8A&e=)
A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks
Online social networks are complex systems often involving millions or even billions of users. Understanding the dynamics of a social network requires analysing characteristics of the network (in its entirety) and the users (as individuals). This paper focuses on calculating userÿÿs social influence, which depends on (i) the userÿÿs positioning in the social network and (ii) interactions between the user and all other users in the social network. Given that data on all users in the social network is required to calculate social influence, something not applicable for todayÿÿs social networks, alternative approaches relying on a limited set of data on users are necessary. However, these approaches introduce uncertainty in calculating (i.e., predicting) the value of social influence. Hence, a methodology is proposed for evaluating algorithms that calculate social influence in complex social networks; this is done by identifying the most accurate and precise algorithm. The proposed
methodology extends the traditional ground truth approach, often used in descriptive statistics and machine learning. Use of the proposed methodology is demonstrated using a case study incorporating four algorithms for calculating a userÿÿs social influence.
Complexity
Volume 2018, Article ID 1084795, 20 pages
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dcc5b204963-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=2fPvgX9KSWiqiqxPt_Yoz2qZU7ru3QEwQpeEvKxsWLY&e=
A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks
Vanja Smailovic, Vedran Podobnik and Ignac Lovrek
Source: www.hindawi.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dccf05067d3-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=OE726GaiA2441fxpJvO5F6SrAELeX9kWFwYNjVh7O3s&s=tkNjiGGtWN1sSQH-6plm9K56dN7ocEAL72Rp7JeUyGY&e=)
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Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.
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