Abstract
It is found that networks in real world divide naturally into communities or modules. Many community detection algorithms have been developed to uncover the community structure in networks. However, most of them focus on non-overlapping communities and the applicability of these work is limited when it comes to real world networks, which inherently are overlapping in most cases, e.g. Facebook and Weibo. In this paper, we propose an overlapping community detection algorithm based on edge representation learning. Firstly, we sample a series of edge sequences using random walks on graph, then a mapping function from edge to feature vectors is automatically learned in an unsupervised way. At last we employ the traditional clustering algorithms, e.g. K-means and its variants, on the learned representations to carry out community detection. To demonstrate the effectiveness of our proposed method, extensive experiments are conducted on a group of synthetic networks and two real world networks with ground truth. Experiment results show that our proposed method outperforms traditional algorithms in terms of evaluation metrics.
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Acknowledgments
The work presented in this paper was supported in part by the Special Project for Independent Innovation and Achievement Transformation of Shandong Province (2013ZHZX2C0102, 2014ZZCX03401).
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Li, S., Zhang, H., Wu, D., Zhang, C., Yuan, D. (2018). Edge Representation Learning for Community Detection in Large Scale Information Networks. In: Doulkeridis, C., Vouros, G., Qu, Q., Wang, S. (eds) Mobility Analytics for Spatio-Temporal and Social Data. MATES 2017. Lecture Notes in Computer Science(), vol 10731. Springer, Cham. https://doi.org/10.1007/978-3-319-73521-4_4
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