Learning Community Embedding with Community Detection and Node Embedding on Graphs

TLDR

The paper proposes a framework that jointly learns community embeddings, community detection, and node embeddings by treating communities as primary units. The framework iteratively couples community detection, node embedding, and community embedding, using node embeddings to refine communities and community embeddings to enhance node embeddings through community‑aware high‑order proximity. Experiments on multiple real‑world graphs show that the joint framework improves graph visualization and outperforms existing baselines in community detection and node classification.

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.