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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.

With the rapid growth in popularity of social websites, social event detection has become one of the hottest research topics. However, continuously monitoring social events has not been well studied. In this demo, we present a novel system called EventEye to effectively monitor evolving events and visualize their evolving paths, which are discovered from tweet streams. In particular, four event operations are defined for our proposed stream clustering algorithm to capture the evolutions over time and a multi-layer indexing structure is designed to support efficient event search from large-scale event databases. In our system, events are visualized in different views, including evolution graph, timeline, map view, etc.