Efficient graph mining techniques for big data

Recent advances in information science have shown that linked data pervade our society and the natural world around us. Graphs have become increasingly important for representing complicated structures and schema-less data such as those generated by Wikipedia, Freebase, and various social networks. However, existing algorithms cannot handle large graphs efficiently, so fast algorithms are needed. We introduce two fast algorithms for identifying the top-k nodes of personalized PageRank and graph clustering. They outperform previous algorithms in terms of both speed and quality. Personalized PageRank and graph clustering are fundamental to many applications. Our algorithms allow many applications to be processed more efficiently and will help to improve the effectiveness of future applications.