Abstract
Bipartite network is a performance of complex networks,The divided of unilateral node of bipartite network has important practical significance for the study of complex networks of community division. Based on the diffusion probability of information and modules ideas in the network,this paper presents a community divided clustering algorithm (IPS algorithm) for bipartite network unilateral nodes.The algorithm simulates the probability of information transfer in the network,through mutual support value between the nodes in network,selecting the max value as the basis for merger different communities.Follow the module of the definition for division after mapping the bipartite network nodes as a single department unilateral network.Finally,we use actual network test the performance of the algorithm.Experimental results show that,the algorithm can not only accurate divided the unilateral node of bipartite network,But also can get high quality community division.
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Fan, C., Wu, H., Zhang, C. (2015). Community Division of Bipartite Network Based on Information Transfer Probability. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9329. Springer, Cham. https://doi.org/10.1007/978-3-319-24069-5_13
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DOI: https://doi.org/10.1007/978-3-319-24069-5_13
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