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, we propose a neighborhood-based similarity measure that could avoid costly graph isomorphism and edit distance computation. Under this new measure, we prove that subgraph similarity search is NP hard, while graph similarity match is polynomial. By studying the principles behind this measure, we found an information propagation model that is able to convert a large network into a set of multidimensional vectors, where sophisticated indexing and similarity search algorithms are availabl...

• Type: Journal
• Year: 2011
• Where: SIGMOD
• Authors: Arijit Khan, Nan Li, Xifeng Yan, Ziyu Guan, Supriyo Chakraborty, Shu Tao
• Keywords: Database, Graph Isomorphism, Multidimensional Vectors, SIGMOD 2011, Target Network