Centrality-based bipartite local community detection algorithm
Article No.: 62, Pages 1 - 8
Abstract
As a kind of typical complex network, bipartite network has already received many specialized research. Local community detection is very useful for bipartite network, but it has not yet been systematically researched. To guarantee equivalent partitioning (obtaining coincident community structure starting from arbitrary node) for two category nodes in bipartite network, a centrality-based bipartite local community detection (CBLCD) algorithm is proposed inspired by water surface undulation. The algorithm first gets local centralized subgraphs/subnetworks according to resource allocation index R defined in this paper, and then expands and combines these subnetworks, until the ultimate local communities are detected. The experimental results on some typical network datasets show that the algorithm achieved both good accuracy and stability.
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- Centrality-based bipartite local community detection algorithm
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Information & Contributors
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Published In
March 2017
1349 pages
ISBN:9781450347747
DOI:10.1145/3018896
- General Chair:
- Hani Hamdan,
- Program Chairs:
- Homero Toral-Cruz,
- Sedat Akleylek,
- Hamid Mcheick,
- Publications Chair:
- Djallel Eddine Boubiche
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 March 2017
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- Research-article
Funding Sources
- Science Research Project of Liaoning Provincial Department of Education
- China State Scholarship Fund CSC
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ICC '17
ICC '17: Second International Conference on Internet of Things, Data and Cloud Computing
March 22 - 23, 2017
Cambridge, United Kingdom
Acceptance Rates
ICC '17 Paper Acceptance Rate 213 of 590 submissions, 36%;
Overall Acceptance Rate 213 of 590 submissions, 36%
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