Abstract
Community detection plays an important role in a wide range of research topics for social networks. The highly dynamic nature of social platforms, and accordingly the constant updates to the underlying network, all present a serious challenge for efficient maintenance of the identified communities—How to avoid computing from scratch the whole community detection result in face of every update, which constitutes small changes more often than not. To solve this problem, we propose a novel and efficient algorithm to maintain the communities in dynamic social networks by identifying and updating only those vertices whose community memberships are affected. The complexity of our algorithm is independent of the graph size. Experiments across varied datasets demonstrate the superiority of our proposed algorithm in terms of time efficiency and accuracy.
This research is partially funded by the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centres in Singapore Funding Initiative and Pinnacle Lab for Analytics at Singapore Management University, National Natural Science Foundation of China (No. 61332006, 61332014, 61328202, 61572119, U1401256) and the Fundamental Research Funds for the Central Universities of China (No. N150402005, N130504006).
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Qin, H., Yuan, Y., Zhu, F., Wang, G. (2016). Efficient Community Maintenance for Dynamic Social Networks. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_50
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DOI: https://doi.org/10.1007/978-3-319-45817-5_50
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