Many graphs in real-world applications, such as telecommunications networks, social-interaction graphs and co-authorship graphs, contain temporal information. However, existing graph mining algorithms fail to exploit these temporal information and the resulting subgraph patterns do not contain any temporal attribute. In this paper, we study the problem of mining a set of diversified temporal subgraph patterns from a temporal graph, where each subgraph is associated with the time interval that the pattern spans. This problem motivates important applications such as finding social trends in social networks, or detecting temporal hotspots in telecommunications networks. We propose a divide-and-conquer algorithm along with effective pruning techniques, and our approach runs 2 to 3 orders of magnitude faster than a baseline algorithm and obtains high-quality temporal subgraph patterns in real temporal graphs.

Filed under: Mining Rich Data Types | Graph Mining and Social Networks