366 research outputs found
Detecting highly overlapping community structure by greedy clique expansion
In complex networks it is common for each node to belong to several
communities, implying a highly overlapping community structure. Recent advances
in benchmarking indicate that existing community assignment algorithms that are
capable of detecting overlapping communities perform well only when the extent
of community overlap is kept to modest levels. To overcome this limitation, we
introduce a new community assignment algorithm called Greedy Clique Expansion
(GCE). The algorithm identifies distinct cliques as seeds and expands these
seeds by greedily optimizing a local fitness function. We perform extensive
benchmarks on synthetic data to demonstrate that GCE's good performance is
robust across diverse graph topologies. Significantly, GCE is the only
algorithm to perform well on these synthetic graphs, in which every node
belongs to multiple communities. Furthermore, when put to the task of
identifying functional modules in protein interaction data, and college dorm
assignments in Facebook friendship data, we find that GCE performs
competitively.Comment: 10 pages, 7 Figures. Implementation source and binaries available at
http://sites.google.com/site/greedycliqueexpansion
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
Towards Online Multiresolution Community Detection in Large-Scale Networks
The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks
Overlapping Community Detection in Networks: the State of the Art and Comparative Study
This paper reviews the state of the art in overlapping community detection
algorithms, quality measures, and benchmarks. A thorough comparison of
different algorithms (a total of fourteen) is provided. In addition to
community level evaluation, we propose a framework for evaluating algorithms'
ability to detect overlapping nodes, which helps to assess over-detection and
under-detection. After considering community level detection performance
measured by Normalized Mutual Information, the Omega index, and node level
detection performance measured by F-score, we reached the following
conclusions. For low overlapping density networks, SLPA, OSLOM, Game and COPRA
offer better performance than the other tested algorithms. For networks with
high overlapping density and high overlapping diversity, both SLPA and Game
provide relatively stable performance. However, test results also suggest that
the detection in such networks is still not yet fully resolved. A common
feature observed by various algorithms in real-world networks is the relatively
small fraction of overlapping nodes (typically less than 30%), each of which
belongs to only 2 or 3 communities.Comment: This paper (final version) is accepted in 2012. ACM Computing
Surveys, vol. 45, no. 4, 2013 (In press) Contact: [email protected]
Finding overlapping communities in networks by label propagation
We propose an algorithm for finding overlapping community structure in very
large networks. The algorithm is based on the label propagation technique of
Raghavan, Albert, and Kumara, but is able to detect communities that overlap.
Like the original algorithm, vertices have labels that propagate between
neighbouring vertices so that members of a community reach a consensus on their
community membership. Our main contribution is to extend the label and
propagation step to include information about more than one community: each
vertex can now belong to up to v communities, where v is the parameter of the
algorithm. Our algorithm can also handle weighted and bipartite networks. Tests
on an independently designed set of benchmarks, and on real networks, show the
algorithm to be highly effective in recovering overlapping communities. It is
also very fast and can process very large and dense networks in a short time
Overlapping Community Detection Extended from Disjoint Community Structure
Community detection is a hot issue in the study of complex networks. Many community detection algorithms have been put forward in different fields. But most of the existing community detection algorithms are used to find disjoint community structure. In order to make full use of the disjoint community detection algorithms to adapt to the new demand of overlapping community detection, this paper proposes an overlapping community detection algorithm extended from disjoint community structure by selecting overlapping nodes (ONS-OCD). In the algorithm, disjoint community structure with high qualities is firstly taken as input, then, potential members of each community are identified. Overlapping nodes are determined according to the node contribution to the community. Finally, adding overlapping nodes to all communities they belong to and get the final overlapping community structure. ONS-OCD algorithm reduces the computation of judging overlapping nodes by narrowing the scope of the potential member nodes of each community. Experimental results both on synthetic and real networks show that the community detection quality of ONS-OCD algorithm is better than several other representative overlapping community detection algorithms
Overlapping Community Detection using Local Seed Expansion
Communities are usually groups of vertices which have higher probability of being connected to each other than to members of other groups. Community detection in complex networks is one of the most popular topics in social network analysis. While in real networks, a person can be overlapped in multiple communities such as family, friends and colleagues, so overlapping community detection attracts more and more attention. Detecting communities from the local structural information of a small number of seed nodes is the successful methods for overlapping community detection. In this work, we propose an overlapping community detection algorithm using local seed expansion approach. Our local seed expansion algorithm selects the nodes with the highest degree as seed nodes and then locally expand these seeds with their entire vertex neighborhood into overlapping communities using Personalized PageRank algorithm. We use F1_score( node level detection ) and NMI( community level detection ) measures to assess the performances of the proposed algorithm by comparing the proposed algorithmâs detected communities with ground_truth communities on many real_world networks. Experimental results show that our algorithm outperforms over other overlapping community detection methods in terms of accuracy and quality of overlapped communities
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