Ego-centric Graph Pattern Census

There is increasing interest in analyzing networks of all types including social, biological, sensor, computer, and transportation networks. Broadly speaking, we may be interested in global network-wide analysis (e.g., centrality analysis, community detection) where the properties of the entire network are of interest, or local ego-centric analysis where the focus is on studying the properties of nodes (egos) by analyzing their neighborhood sub graphs. In this paper we propose and study ego-centric pattern census queries, a new type of graph analysis query, where a given structural pattern is searched for in every node's neighborhood and the counts are reported or used in further analysis. This kind of analysis is useful in many domains in social network analysis including opinion leader identification, node classification, link prediction, and role identification. We propose an SQL-based declarative language to support this class of queries, and develop a series of efficient query evaluation algorithms for it. We evaluate our algorithms on a variety of synthetically generated graphs. We also show an application of our language in a real-world scenario for predicting future collaborations from DBLP data.