Mining, Indexing, and Searching Graphs in Large Data Sets
Recent research on pattern discovery has progressed from mining frequent itemsets and sequences to mining structured patterns including trees, lattices, and graphs. As a general data structure, graph can model complicated relations among data with wide applications in Web, social network analysis, and bioinformatics. However, mining and searching large graphs in graph databases is challenging due to the presence of an exponential number of frequent subgraphs. In this talk, we present our recent progress on developing efficient and scalable methods for mining and searching of graphs in large databases. We introduce gSpan and CloseGraph, two efficient methods for mining frequent graph patterns in graph databases. Then we introduce constraint-based graph mining methods. Further, we introduce a graph indexing method, gIndex, and a graph approximate searching method, grafil, both taking advantages of frequent graph mining to construct a compact but highly effective graph index and perform similarity search with such indexing structures. These methods not only facilitate mining and querying graph patterns in massive datasets but also claim broad applications in other fields, including DB/OS systems and software engineering.
Author: Jiawei Han, University of Illinois