26 research outputs found
Overlapping Community Discovery Methods: A Survey
The detection of overlapping communities is a challenging problem which is
gaining increasing interest in recent years because of the natural attitude of
individuals, observed in real-world networks, to participate in multiple groups
at the same time. This review gives a description of the main proposals in the
field. Besides the methods designed for static networks, some new approaches
that deal with the detection of overlapping communities in networks that change
over time, are described. Methods are classified with respect to the underlying
principles guiding them to obtain a network division in groups sharing part of
their nodes. For each of them we also report, when available, computational
complexity and web site address from which it is possible to download the
software implementing the method.Comment: 20 pages, Book Chapter, appears as Social networks: Analysis and Case
Studies, A. Gunduz-Oguducu and A. S. Etaner-Uyar eds, Lecture Notes in Social
Networks, pp. 105-125, Springer,201
Detection of node group membership in networks with group overlap
Most networks found in social and biochemical systems have modular
structures. An important question prompted by the modularity of these networks
is whether nodes can be said to belong to a single group. If they cannot, we
would need to consider the role of "overlapping communities." Despite some
efforts in this direction, the problem of detecting overlapping groups remains
unsolved because there is neither a formal definition of overlapping community,
nor an ensemble of networks with which to test the performance of group
detection algorithms when nodes can belong to more than one group. Here, we
introduce an ensemble of networks with overlapping groups. We then apply three
group identification methods--modularity maximization, k-clique percolation,
and modularity-landscape surveying--to these networks. We find that the
modularity-landscape surveying method is the only one able to detect
heterogeneities in node memberships, and that those heterogeneities are only
detectable when the overlap is small. Surprisingly, we find that the k-clique
percolation method is unable to detect node membership for the overlapping
case.Comment: 12 pages, 6 figures. To appear in Euro. Phys. J
Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches
We implemented three recently proposed approaches to the identification of
overlapping and hierarchical substructures in graphs and applied the
corresponding algorithms to a network of 492 information-science papers coupled
via their cited sources. The thematic substructures obtained and overlaps
produced by the three hierarchical cluster algorithms were compared to a
content-based categorisation, which we based on the interpretation of titles
and keywords. We defined sets of papers dealing with three topics located on
different levels of aggregation: h-index, webometrics, and bibliometrics. We
identified these topics with branches in the dendrograms produced by the three
cluster algorithms and compared the overlapping topics they detected with one
another and with the three pre-defined paper sets. We discuss the advantages
and drawbacks of applying the three approaches to paper networks in research
fields.Comment: 18 pages, 9 figure
Neighborhood Overlapped Propagation Algorithm For Community Detection Based On Label Time-Sequence
The community detection algorithms based on label propagation (LPA) receive broad attention for the advantages of near-linear complexity and no prerequisite for any object function or cluster number. However, the propagation of labels contains uncertainty and randomness, which affects the accuracy and stability of the LPA algorithm. In this study, we propose an efficient detection method based on COPRA with Time-sequence (COPRA_TS). Firstly, the labels are sorted according to a new label importance measure. Then, the label of each vertex is updated according to time-sequence topology measure. The experiments on both the artificial datasets and the real-world datasets demonstrate that the quality of communities discovered by COPRA_TS algorithm is improved with a better stability. At last some future research topics are given
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