1,322 research outputs found
A maximal clique based multiobjective evolutionary algorithm for overlapping community detection
Detecting community structure has become one im-portant technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can be-long to only one community. However, in many real-world net-works, communities are often overlapped with each other. De-veloping overlapping community detection algorithms thus be-comes necessary. Along this avenue, this paper proposes a maxi-mal clique based multiobjective evolutionary algorithm for over-lapping community detection. In this algorithm, a new represen-tation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme al-lows multiobjective evolutionary algorithms to handle the over-lapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm
Community detection algorithms: a comparative analysis
Uncovering the community structure exhibited by real networks is a crucial
step towards an understanding of complex systems that goes beyond the local
organization of their constituents. Many algorithms have been proposed so far,
but none of them has been subjected to strict tests to evaluate their
performance. Most of the sporadic tests performed so far involved small
networks with known community structure and/or artificial graphs with a
simplified structure, which is very uncommon in real systems. Here we test
several methods against a recently introduced class of benchmark graphs, with
heterogeneous distributions of degree and community size. The methods are also
tested against the benchmark by Girvan and Newman and on random graphs. As a
result of our analysis, three recent algorithms introduced by Rosvall and
Bergstrom, Blondel et al. and Ronhovde and Nussinov, respectively, have an
excellent performance, with the additional advantage of low computational
complexity, which enables one to analyze large systems.Comment: 12 pages, 8 figures. The software to compute the values of our
general normalized mutual information is available at
http://santo.fortunato.googlepages.com/inthepress
Clique Graphs and Overlapping Communities
It is shown how to construct a clique graph in which properties of cliques of
a fixed order in a given graph are represented by vertices in a weighted graph.
Various definitions and motivations for these weights are given. The detection
of communities or clusters is used to illustrate how a clique graph may be
exploited. In particular a benchmark network is shown where clique graphs find
the overlapping communities accurately while vertex partition methods fail.Comment: 23 pages plus 16 additional pages in appendice
Communities as Well Separated Subgraphs With Cohesive Cores: Identification of Core-Periphery Structures in Link Communities
Communities in networks are commonly considered as highly cohesive subgraphs
which are well separated from the rest of the network. However, cohesion and
separation often cannot be maximized at the same time, which is why a
compromise is sought by some methods. When a compromise is not suitable for the
problem to be solved it might be advantageous to separate the two criteria. In
this paper, we explore such an approach by defining communities as well
separated subgraphs which can have one or more cohesive cores surrounded by
peripheries. We apply this idea to link communities and present an algorithm
for constructing hierarchical core-periphery structures in link communities and
first test results.Comment: 12 pages, 2 figures, submitted version of a paper accepted for the
7th International Conference on Complex Networks and Their Applications,
December 11-13, 2018, Cambridge, UK; revised version at
http://141.20.126.227/~qm/papers
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
Egomunities, Exploring Socially Cohesive Person-based Communities
In the last few years, there has been a great interest in detecting
overlapping communities in complex networks, which is understood as dense
groups of nodes featuring a low outbound density. To date, most methods used to
compute such communities stem from the field of disjoint community detection by
either extending the concept of modularity to an overlapping context or by
attempting to decompose the whole set of nodes into several possibly
overlapping subsets. In this report we take an orthogonal approach by
introducing a metric, the cohesion, rooted in sociological considerations. The
cohesion quantifies the community-ness of one given set of nodes, based on the
notions of triangles - triplets of connected nodes - and weak ties, instead of
the classical view using only edge density. A set of nodes has a high cohesion
if it features a high density of triangles and intersects few triangles with
the rest of the network. As such, we introduce a numerical characterization of
communities: sets of nodes featuring a high cohesion. We then present a new
approach to the problem of overlapping communities by introducing the concept
of ego-munities, which are subjective communities centered around a given node,
specifically inside its neighborhood. We build upon the cohesion to construct a
heuristic algorithm which outputs a node's ego-munities by attempting to
maximize their cohesion. We illustrate the pertinence of our method with a
detailed description of one person's ego-munities among Facebook friends. We
finally conclude by describing promising applications of ego-munities such as
information inference and interest recommendations, and present a possible
extension to cohesion in the case of weighted networks
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