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   Barry Wellman

    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
   NetLab Network                 FRSC                      INSNA Founder           twitter: @barrywellman
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman

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Date: Mon, 28 Nov 2016 12:04:27 +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

The many facets of community detection in complex networks

    Community detection, the decomposition of a graph into meaningful building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of community structure, and classified based on the mathematical techniques they employ. However, this can be misleading because apparent similarities in their mathematical machinery can disguise entirely different objectives. Here we provide a focused review of the different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research, and points out open directions and avenues for future research.

The many facets of community detection in complex networks
Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte

Source: (

Untangling the role of diverse social dimensions in the diffusion of microfinance

    Ties between individuals on a social network can represent different 
dimensions of interactions, and the spreading of information and 
innovations on these networks could potentially be driven by some 
dimensions more than by others. In this paper we investigate this issue by 
studying the diffusion of microfinance within rural India villages and 
accounting for the whole multilayer structure of the underlying social 
networks. We define a new measure of node centrality, diffusion 
versatility, and show that this is a better predictor of microfinance 
participation rate than previously introduced measures defined on 
aggregated single-layer social networks. Moreover, we untangle the role 
played by each social dimension and find that the most prominent role is 
played by the nodes that are central on layers concerned with trust, 
shedding new light on the key triggers of the diffusion of microfinance.

Untangling the role of diverse social dimensions in the diffusion of microfinance
Elisa Omodei and Alex Arenas
Applied Network Science20161:14
DOI: 10.1007/s41109-016-0016-x

Source: (

The impact of anticipation in dynamical systems

    The flocking of animals is often modelled as a dynamical system, in 
which individuals are represented as particles whose interactions are 
determined by the current state of the system. Many animals, however, 
including humans, have predictive capabilities, and presumably base their 
behavioural decisions---at least partially---upon an anticipated state of 
their environment. We explore a minimal version of this idea in the 
context of particles that interact according to a pairwise potential. 
Anticipation enters the picture by calculating the interparticle forces 
from linear extrapolation of the positions some time ˇˇ into the future. 
Our analysis shows that for intermediate values of ˇˇ the particles 
rapidly form milling structures, induced by velocity alignment that 
emerges from the prediction. We also show that for ˇˇ>0, any dynamical 
system governed by an even potential becomes dissipative. These results 
suggest that anticipation could play an important role in collective 
behaviour, since it induces pattern formation and stabilises the dynamics 
of the system.

The impact of anticipation in dynamical systems
P. Gerlee, K. TunstrÝm, T. Lundh, B. Wennberg

Source: (

Collective navigation of complex networks: Participatory greedy routing

    Many networks are used to transfer information or goods, in other 
words, they are navigated. The larger the network, the more difficult it 
is to navigate efficiently. Indeed, information routing in the Internet 
faces serious scalability problems due to its rapid growth, recently 
accelerated by the rise of the Internet of Things. Large networks like the 
Internet can be navigated efficiently if nodes, or agents, actively 
forward information based on hidden maps underlying these systems. 
However, in reality most agents will deny to forward messages, which has a 
cost, and navigation is impossible. Can we design appropriate incentives 
that lead to participation and global navigability? Here, we present an 
evolutionary game where agents share the value generated by successful 
delivery of information or goods. We show that global navigability can 
emerge, but its complete breakdown is possible as well. Furthermore, we 
show that the system tends to self-organize into local clusters of agents 
who participate in the navigation. This organizational principle can be 
exploited to favor the emergence of global navigability in the system.

Collective navigation of complex networks: Participatory greedy routing

Kaj-Kolja Kleineberg, Dirk Helbing

Source: (

LANET 2017 ˇˇ Latin American Conference on Complex Networks 2017

    September 25-29, 2017, Puebla, Mexico

The aim of LANET is to provide with a forum to join all scientists who are somehow related to the research on Network Science in Latin America.

The rapid growth of the field of Network Science in the last two decades has manifested in the form of schools, workshops and conferences in Latin America. However, the creation of LANET as a stable and periodic forum devoted to Network Science will further spur the formation of research groups interested in the field and help to establish it as a discipline across Latin American Universities and Research Institutions.

Source: (

A Data Driven Network Approach to Rank Countries Production Diversity and Food Specialization

    By Chengyi Tu, Joel Carr & Samir Suweis

The easy access to large data sets has allowed for leveraging methodology in network physics and complexity science to disentangle patterns and processes directly from the data, leading to key insights in the behavior of systems. Here we use country specific food production data to study binary and weighted topological properties of the bipartite country-food production matrix. This country-food production matrix can be: 1) transformed into overlap matrices which embed information regarding shared production of products among countries, and or shared countries for individual products, 2) identify subsets of countries which produce similar commodities or subsets of commodities shared by a given country allowing for visualization of correlations in large networks, and 3) used to rank country fitness (the ability to produce a diverse array of products weighted on the type of food commodities) and food specialization (quantified on the number of countries producing a specific
food product weighted on their fitness). Our results show that, on average, countries with high fitness produce both low and high specializion food commodities, whereas nations with low fitness tend to produce a small basket of diverse food products, typically comprised of low specializion food commodities.

Source: (

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

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