Abstract
Most of the community detection algorithms underperform on overlapping community structures. To eliminate the ambiguity, we define a membership function which can compute a node’s subordinate level to communities. This paper proposed a heuristic community detection algorithm with membership function (MCDA) utilizing which as a measure for community detection. By computing one node’s membership to each community, we can find which community it belongs to. Considering the edge connectivity, information transmission efficiency and other factors, some experiments are taken to demonstrate that the proposed algorithm perform higher accuracy and lower time complexity.
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Acknowledgment
This work is partially supported by the Science Research Project of Liaoning Provincial Department of Education under Grant No. L2015173, the scholarship under China State Scholarship Fund CSC No. 201606085034.
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Chen, D., Jia, L., Sima, D., Huang, X., Wang, D. (2017). Community Detection Algorithm with Membership Function. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-10-3966-9_20
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DOI: https://doi.org/10.1007/978-981-10-3966-9_20
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