1. Learning Community Embedding with Community Detection and Node Embedding on Graphs
- Author
-
Vincent W. Zheng, Erik Cambria, Sandro Cavallari, Hongyun Cai, and Kevin Chen-Chuan Chang
- Subjects
Theoretical computer science ,Graph drawing ,Computer science ,Graph embedding ,020204 information systems ,Learning community ,Node (networking) ,0202 electrical engineering, electronic engineering, information engineering ,Embedding ,020201 artificial intelligence & image processing ,Network representation learning ,02 engineering and technology ,Graph - Abstract
In this paper, we study an important yet largely under-explored setting of graph embedding, i.e., embedding communities instead of each individual nodes. We find that community embedding is not only useful for community-level applications such as graph visualization, but also beneficial to both community detection and node classification. To learn such embedding, our insight hinges upon a closed loop among community embedding, community detection and node embedding. On the one hand, node embedding can help improve community detection, which outputs good communities for fitting better community embedding. On the other hand, community embedding can be used to optimize the node embedding by introducing a community-aware high-order proximity. Guided by this insight, we propose a novel community embedding framework that jointly solves the three tasks together. We evaluate such a framework on multiple real-world datasets, and show that it improves graph visualization and outperforms state-of-the-art baselines in various application tasks, e.g., community detection and node classification.
- Published
- 2017