Efficient processing of group-oriented connection queries in a large graph.

We study query processing in large graphs that are fundamental data model underpinning various social networks and Web structures. Given a set of query nodes, we aim to find the groups which the query nodes belong to, as well as the best connection among the groups. Such a query is useful to many applications but the query processing is extremely costly. We define a new notion of Correlation Group (CG), which is a set of nodes that are strongly correlated in a large graph G. We then extract the subgraph from G that gives the best connection for the nodes in a CG. To facilitate query processing, we develop an efficient index built upon the CGs. Our experiments show that the CGs are meaningful as groups and importantly, the meaningfulness of the query results are justifiable. We also demonstrate the high efficiency of CG computation, index construction and query processing.