174,734 research outputs found
Communities and beyond: mesoscopic analysis of a large social network with complementary methods
Community detection methods have so far been tested mostly on small empirical
networks and on synthetic benchmarks. Much less is known about their
performance on large real-world networks, which nonetheless are a significant
target for application. We analyze the performance of three state-of-the-art
community detection methods by using them to identify communities in a large
social network constructed from mobile phone call records. We find that all
methods detect communities that are meaningful in some respects but fall short
in others, and that there often is a hierarchical relationship between
communities detected by different methods. Our results suggest that community
detection methods could be useful in studying the general mesoscale structure
of networks, as opposed to only trying to identify dense structures.Comment: 11 pages, 10 figures. V2: typos corrected, one sentence added. V3:
revised version, Appendix added. V4: final published versio
Fast Multi-Scale Community Detection based on Local Criteria within a Multi-Threaded Algorithm
Many systems can be described using graphs, or networks. Detecting
communities in these networks can provide information about the underlying
structure and functioning of the original systems. Yet this detection is a
complex task and a large amount of work was dedicated to it in the past decade.
One important feature is that communities can be found at several scales, or
levels of resolution, indicating several levels of organisations. Therefore
solutions to the community structure may not be unique. Also networks tend to
be large and hence require efficient processing. In this work, we present a new
algorithm for the fast detection of communities across scales using a local
criterion. We exploit the local aspect of the criterion to enable parallel
computation and improve the algorithm's efficiency further. The algorithm is
tested against large generated multi-scale networks and experiments demonstrate
its efficiency and accuracy.Comment: arXiv admin note: text overlap with arXiv:1204.100
Characterizing the community structure of complex networks
Community structure is one of the key properties of complex networks and
plays a crucial role in their topology and function. While an impressive amount
of work has been done on the issue of community detection, very little
attention has been so far devoted to the investigation of communities in real
networks. We present a systematic empirical analysis of the statistical
properties of communities in large information, communication, technological,
biological, and social networks. We find that the mesoscopic organization of
networks of the same category is remarkably similar. This is reflected in
several characteristics of community structure, which can be used as
``fingerprints'' of specific network categories. While community size
distributions are always broad, certain categories of networks consist mainly
of tree-like communities, while others have denser modules. Average path
lengths within communities initially grow logarithmically with community size,
but the growth saturates or slows down for communities larger than a
characteristic size. This behaviour is related to the presence of hubs within
communities, whose roles differ across categories. Also the community
embeddedness of nodes, measured in terms of the fraction of links within their
communities, has a characteristic distribution for each category. Our findings
are verified by the use of two fundamentally different community detection
methods.Comment: 15 pages, 20 figures, 4 table
Spatial correlations in attribute communities
Community detection is an important tool for exploring and classifying the
properties of large complex networks and should be of great help for spatial
networks. Indeed, in addition to their location, nodes in spatial networks can
have attributes such as the language for individuals, or any other
socio-economical feature that we would like to identify in communities. We
discuss in this paper a crucial aspect which was not considered in previous
studies which is the possible existence of correlations between space and
attributes. Introducing a simple toy model in which both space and node
attributes are considered, we discuss the effect of space-attribute
correlations on the results of various community detection methods proposed for
spatial networks in this paper and in previous studies. When space is
irrelevant, our model is equivalent to the stochastic block model which has
been shown to display a detectability-non detectability transition. In the
regime where space dominates the link formation process, most methods can fail
to recover the communities, an effect which is particularly marked when
space-attributes correlations are strong. In this latter case, community
detection methods which remove the spatial component of the network can miss a
large part of the community structure and can lead to incorrect results.Comment: 10 pages and 7 figure
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