Challenges and Advances on Graph Mining

Philip S. Yu

Mining graph data has become an important and active research topic in the last decade, which has a wide variety of scientific and commercial applications, such as in bioinformatics, security, the web, and social networks. Previous research on graph classification mainly focuses on mining significant subgraph features under single label settings for supervised learning. The basic assumption is that a large number of labeled graphs are available. However, labeling graph data is quite expensive and time consuming for many real-world applications. In this talk, we examine the challenges and alternative mining approaches to reduce the labeling cost on graph data.

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