Abstract
The mining of communities in bipartite network has theory significance and application value to complex networks. The community mining problem of bipartite networks is transformed into a bidirectional clustering problem for 0–1 matrices. A new modularity measurement standard is proposed, and the ant colony optimization algorithm is applied to the mining of communities in bipartite networks. In this algorithm, each ant adds some rows and columns to the solution according to the probability formula. By repeating a certain number of times, the ant gets a solution corresponding to a community. The algorithm measures the quality of the solution according to the modularity and updates the pheromone. The algorithm preserves k solutions with the highest modularity obtained by all ants in each time, and selects the optimal k solutions as the division of communities by ant. The algorithm does not need to determine the number of communities in advance and can automatically identify the exact number of actual network communities. Experimental results show that the proposed algorithm can not only obtain high quality partition results, but also mine multiple overlapping communities.
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Acknowledgements
This research was supported in part by the Chinese National Natural Science Foundation under Grant Nos. 61602202 and 61702441, the Natural Science Foundation of Jiangsu Province under contracts BK20160428 and BK20161302.
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Chen, BL., Yuan, Y., Zhang, YJ., Li, FF., Yu, Q. (2019). Measurement and Algorithm for Overlapping Community Partitioning in Bipartite Networks. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_38
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DOI: https://doi.org/10.1007/978-3-319-93659-8_38
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