47,525 research outputs found
Detecting Communities with Different Sizes for Social Network Analysis
National Natural Science Foundation of China; Yunnan Educational Department Foundation; Program for Young and Middle-aged Skeleton Teachers, Yunnan Universit
Detecting Communities in Networks by Merging Cliques
Many algorithms have been proposed for detecting disjoint communities
(relatively densely connected subgraphs) in networks. One popular technique is
to optimize modularity, a measure of the quality of a partition in terms of the
number of intracommunity and intercommunity edges. Greedy approximate
algorithms for maximizing modularity can be very fast and effective. We propose
a new algorithm that starts by detecting disjoint cliques and then merges these
to optimize modularity. We show that this performs better than other similar
algorithms in terms of both modularity and execution speed.Comment: 5 pages, 7 figure
Communities in Networks
We survey some of the concepts, methods, and applications of community
detection, which has become an increasingly important area of network science.
To help ease newcomers into the field, we provide a guide to available
methodology and open problems, and discuss why scientists from diverse
backgrounds are interested in these problems. As a running theme, we emphasize
the connections of community detection to problems in statistical physics and
computational optimization.Comment: survey/review article on community structure in networks; published
version is available at
http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd
Limited resolution and multiresolution methods in complex network community detection
Detecting community structure in real-world networks is a challenging
problem. Recently, it has been shown that the resolution of methods based on
optimizing a modularity measure or a corresponding energy is limited;
communities with sizes below some threshold remain unresolved. One possibility
to go around this problem is to vary the threshold by using a tuning parameter,
and investigate the community structure at variable resolutions. Here, we
analyze the resolution limit and multiresolution behavior for two different
methods: a q-state Potts method proposed by Reichard and Bornholdt, and a
recent multiresolution method by Arenas, Fernandez, and Gomez. These methods
are studied analytically, and applied to three test networks using simulated
annealing.Comment: 6 pages, 2 figures.Minor changes from previous version, shortened a
couple of page
Comparative Evaluation of Community Detection Algorithms: A Topological Approach
Community detection is one of the most active fields in complex networks
analysis, due to its potential value in practical applications. Many works
inspired by different paradigms are devoted to the development of algorithmic
solutions allowing to reveal the network structure in such cohesive subgroups.
Comparative studies reported in the literature usually rely on a performance
measure considering the community structure as a partition (Rand Index,
Normalized Mutual information, etc.). However, this type of comparison neglects
the topological properties of the communities. In this article, we present a
comprehensive comparative study of a representative set of community detection
methods, in which we adopt both types of evaluation. Community-oriented
topological measures are used to qualify the communities and evaluate their
deviation from the reference structure. In order to mimic real-world systems,
we use artificially generated realistic networks. It turns out there is no
equivalence between both approaches: a high performance does not necessarily
correspond to correct topological properties, and vice-versa. They can
therefore be considered as complementary, and we recommend applying both of
them in order to perform a complete and accurate assessment
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