Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers
Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting Multi-facet Community Structure from Bipartite Networks
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Exploit the tripartite network of social tagging for web clustering
Proceedings of the 18th ACM conference on Information and knowledge management
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Detecting communities from tripartite networks
Proceedings of the 19th international conference on World wide web
Networks: An Introduction
Modularity for heterogeneous networks
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Detecting community structure in bipartite networks based on matrix factorisation
International Journal of Wireless and Mobile Computing
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Heterogeneous systems in nature are often characterized by the mesoscopic structure known as communities. In this paper, we propose a framework to address the problem of community detection in bipartite networks and tripartite hypernetworks, which are appropriate models for many heterogeneous systems. The most important advantage of our method is that it is competent for detecting both communities of one-to-one correspondence and communities of many-to-many correspondence, while state of the art techniques can only handle the former. We demonstrate this advantage and show other desired properties of our method through extensive experiments in both synthetic and real-world datasets.