Learning Community Embedding with Community Detection and Node Embedding on Graphs

• DOI: 10.1145/3132847.3132925
• Publication Date: 2017-11-06T13:30:29Z
• Authors: Sandro Cavallari; Vincent W. Zheng; Hongyun Cai; Kevin Chen-Chuan ...; Erik Cambria
• Keywords: Graph Embedding

In this paper, we study an important yet largely under-explored setting of graph embedding, i.e., embedding communities instead each individual nodes. We find that community is not only useful for community-level applications such as visualization, but also beneficial to both detection and node classification. To learn our insight hinges upon a closed loop among embedding. On the one hand, can help improve detection, which outputs good fitting better other be used optimize by introducing community-aware high-order proximity. Guided insight, propose novel framework jointly solves three tasks together. evaluate on multiple real-world datasets, show it improves visualization outperforms state-of-the-art baselines in various application tasks, e.g.,