52,386 research outputs found
Ordered community structure in networks
Community structure in networks is often a consequence of homophily, or
assortative mixing, based on some attribute of the vertices. For example,
researchers may be grouped into communities corresponding to their research
topic. This is possible if vertex attributes have discrete values, but many
networks exhibit assortative mixing by some continuous-valued attribute, such
as age or geographical location. In such cases, no discrete communities can be
identified. We consider how the notion of community structure can be
generalized to networks that are based on continuous-valued attributes: in
general, a network may contain discrete communities which are ordered according
to their attribute values. We propose a method of generating synthetic ordered
networks and investigate the effect of ordered community structure on the
spread of infectious diseases. We also show that community detection algorithms
fail to recover community structure in ordered networks, and evaluate an
alternative method using a layout algorithm to recover the ordering.Comment: This is an extended preprint version that includes an extra example:
the college football network as an ordered (spatial) network. Further
improvements, not included here, appear in the journal version. Original
title changed (from "Ordered and continuous community structure in networks")
to match journal versio
Community-based Immunization Strategies for Epidemic Control
Understanding the epidemic dynamics, and finding out efficient techniques to
control it, is a challenging issue. A lot of research has been done on targeted
immunization strategies, exploiting various global network topological
properties. However, in practice, information about the global structure of the
contact network may not be available. Therefore, immunization strategies that
can deal with a limited knowledge of the network structure are required. In
this paper, we propose targeted immunization strategies that require
information only at the community level. Results of our investigations on the
SIR epidemiological model, using a realistic synthetic benchmark with
controlled community structure, show that the community structure plays an
important role in the epidemic dynamics. An extensive comparative evaluation
demonstrates that the proposed strategies are as efficient as the most
influential global centrality based immunization strategies, despite the fact
that they use a limited amount of information. Furthermore, they outperform
alternative local strategies, which are agnostic about the network structure,
and make decisions based on random walks.Comment: 6 pages, 7 figure
Fast community structure local uncovering by independent vertex-centred process
This paper addresses the task of community detection and proposes a local
approach based on a distributed list building, where each vertex broadcasts
basic information that only depends on its degree and that of its neighbours. A
decentralised external process then unveils the community structure. The
relevance of the proposed method is experimentally shown on both artificial and
real data.Comment: 2015 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, Aug 2015, Paris, France. Proceedings of the 2015
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Minin
Flow networks: A characterization of geophysical fluid transport
We represent transport between different regions of a fluid domain by flow
networks, constructed from the discrete representation of the Perron-Frobenius
or transfer operator associated to the fluid advection dynamics. The procedure
is useful to analyze fluid dynamics in geophysical contexts, as illustrated by
the construction of a flow network associated to the surface circulation in the
Mediterranean sea. We use network-theory tools to analyze the flow network and
gain insights into transport processes. In particular we quantitatively relate
dispersion and mixing characteristics, classically quantified by Lyapunov
exponents, to the degree of the network nodes. A family of network entropies is
defined from the network adjacency matrix, and related to the statistics of
stretching in the fluid, in particular to the Lyapunov exponent field. Finally
we use a network community detection algorithm, Infomap, to partition the
Mediterranean network into coherent regions, i.e. areas internally well mixed,
but with little fluid interchange between them.Comment: 16 pages, 15 figures. v2: published versio
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