Adversarial Learning for Overlapping Community Detection and Network Embedding

Junyang Chen, Zhiguo Gong, Quanyu Dai, Chunyuan Yuan, Weiwen Liu

Abstract: Network Embedding (NE) aims at modeling network graph by encoding vertices and edges into a low-dimensional space. These learned vectors which preserve proximities can be used for subsequent applications, such as vertex classification and link prediction. Skip-gram with negative sampling is the most widely used method for existing NE models to approximate their objective functions. However, this method only focuses on learning representation from the local connectivity of vertices (i.e., neighbors). In real-world scenarios, a vertex may have multifaceted aspects and should belong to overlapping communities. For example, in a social network, a user may subscribe to political, economic and sports channels simultaneously, but the politics share more common attributes with the economy and less with the sports. In this paper, we propose an adversarial learning approach for modeling overlapping communities of vertices. Each community and vertex are mapped into an embedding space, while we also learn the association between each pair of community and vertex. The experimental results show that our proposed model not only can outperform the state-of-the-art (including GANs-based) models on vertex classification tasks but also can achieve superior performances on overlapping community detection.