11,978 research outputs found
Overlapping modularity at the critical point of k-clique percolation
One of the most remarkable social phenomena is the formation of communities
in social networks corresponding to families, friendship circles, work teams,
etc. Since people usually belong to several different communities at the same
time, the induced overlaps result in an extremely complicated web of the
communities themselves. Thus, uncovering the intricate community structure of
social networks is a non-trivial task with great potential for practical
applications, gaining a notable interest in the recent years. The Clique
Percolation Method (CPM) is one of the earliest overlapping community finding
methods, which was already used in the analysis of several different social
networks. In this approach the communities correspond to k-clique percolation
clusters, and the general heuristic for setting the parameters of the method is
to tune the system just below the critical point of k-clique percolation.
However, this rule is based on simple physical principles and its validity was
never subject to quantitative analysis. Here we examine the quality of the
partitioning in the vicinity of the critical point using recently introduced
overlapping modularity measures. According to our results on real social- and
other networks, the overlapping modularities show a maximum close to the
critical point, justifying the original criteria for the optimal parameter
settings.Comment: 20 pages, 6 figure
Coding Opportunity Densification Strategies for Instantly Decodable Network Coding
In this paper, we aim to identify the strategies that can maximize and
monotonically increase the density of the coding opportunities in instantly
decodable network coding (IDNC).Using the well-known graph representation of
IDNC, first derive an expression for the exact evolution of the edge set size
after the transmission of any arbitrary coded packet. From the derived
expressions, we show that sending commonly wanted packets for all the receivers
can maximize the number of coding opportunities. Since guaranteeing such
property in IDNC is usually impossible, this strategy does not guarantee the
achievement of our target. Consequently, we further investigate the problem by
deriving the expectation of the edge set size evolution after ignoring the
identities of the packets requested by the different receivers and considering
only their numbers. We then employ this expected expression to show that
serving the maximum number of receivers having the largest numbers of missing
packets and erasure probabilities tends to both maximize and monotonically
increase the expected density of coding opportunities. Simulation results
justify our theoretical findings. Finally, we validate the importance of our
work through two case studies showing that our identified strategy outperforms
the step-by-step service maximization solution in optimizing both the IDNC
completion delay and receiver goodput
Sharp transition towards shared vocabularies in multi-agent systems
What processes can explain how very large populations are able to converge on
the use of a particular word or grammatical construction without global
coordination? Answering this question helps to understand why new language
constructs usually propagate along an S-shaped curve with a rather sudden
transition towards global agreement. It also helps to analyze and design new
technologies that support or orchestrate self-organizing communication systems,
such as recent social tagging systems for the web. The article introduces and
studies a microscopic model of communicating autonomous agents performing
language games without any central control. We show that the system undergoes a
disorder/order transition, going trough a sharp symmetry breaking process to
reach a shared set of conventions. Before the transition, the system builds up
non-trivial scale-invariant correlations, for instance in the distribution of
competing synonyms, which display a Zipf-like law. These correlations make the
system ready for the transition towards shared conventions, which, observed on
the time-scale of collective behaviors, becomes sharper and sharper with system
size. This surprising result not only explains why human language can scale up
to very large populations but also suggests ways to optimize artificial
semiotic dynamics.Comment: 12 pages, 4 figure
Bounded Confidence under Preferential Flip: A Coupled Dynamics of Structural Balance and Opinions
In this work we study the coupled dynamics of social balance and opinion
formation. We propose a model where agents form opinions under bounded
confidence, but only considering the opinions of their friends. The signs of
social ties -friendships and enmities- evolve seeking for social balance,
taking into account how similar agents' opinions are. We consider both the case
where opinions have one and two dimensions. We find that our dynamics produces
the segregation of agents into two cliques, with the opinions of agents in one
clique differing from those in the other. Depending on the level of bounded
confidence, the dynamics can produce either consensus of opinions within each
clique or the coexistence of several opinion clusters in a clique. For the
uni-dimensional case, the opinions in one clique are all below the opinions in
the other clique, hence defining a "left clique" and a "right clique". In the
two-dimensional case, our numerical results suggest that the two cliques are
separated by a hyperplane in the opinion space. We also show that the
phenomenon of unidimensional opinions identified by DeMarzo, Vayanos and
Zwiebel (Q J Econ 2003) extends partially to our dynamics. Finally, in the
context of politics, we comment about the possible relation of our results to
the fragmentation of an ideology and the emergence of new political parties.Comment: 8 figures, PLoS ONE 11(10): e0164323, 201
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