TY - JOUR ID - 104603 TI - Detecting Overlapping Communities in Social Networks using Deep Learning JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Salehi, S. M. M AU - Pouyan, A. A. AD - Department of Computer Engineering, Shahrood University of Technology, Shahrood, Iran. Y1 - 2020 PY - 2020 VL - 33 IS - 3 SP - 366 EP - 376 KW - community detection KW - Overlapping Communities KW - Deep Learning KW - Social Networks KW - Graph embedding DO - 10.5829/ije.2020.33.03c.01 N2 - In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping and disjoint detection of community in networks. In recent years, many researchers have concentrated on feature learning and network embedding methods for node clustering. These methods map the network into a lower-dimensional representation space. We propose a model in this research for learning graph representation using deep neural networks. In this method, a nonlinear embedding of the original graph is fed to stacked auto-encoders for learning the model. Then an overlapping clustering algorithm is performed to obtain overlapping communities. The effectiveness of the proposed model is investigated by conducting experiments on standard benchmarks and real-world datasets of varying sizes. Empirical results exhibit that the presented method outperforms some popular community detection methods. UR - https://www.ije.ir/article_104603.html L1 - https://www.ije.ir/article_104603_f3eda437059359276a9a5265732f007c.pdf ER -