Neighborhood Based Fast Graph Search in Large Networks
Complex social and information network search becomes important with a variety of applications. In the core of these applications, lies a common and critical problem: given a labeled network and a query graph, how to efficiently search the query graph in the target network. The presence of noise and the incomplete knowledge about the structure and content of the target network make it unrealistic to find an exact match. Rather, it is more appealing to find the top-k approximate matches. In this paper, the authors propose a neighborhood-based similarity measure that could avoid costly graph isomorphism and edit distance computation.