Abstract

We focus on large graphs where nodes have attributes, such as a social network where the nodes are labelled with each person’s job title. In such a setting, we want to find subgraphs that match a user query pattern. For example, a ‘star ’ query would be, “find a CEO who has strong interactions with a Manager, a Lawyer, and an Accountant, or another structure as close to that as possible”. Similarly, a ‘loop ’ query could help spot a money laundering ring. Traditional SQL-based methods, as well as more recent graph indexing methods, will return no answer when an exact match does not exist. Our method can find exact-, as well as near-matches, and it will present them to the user in our proposed ‘goodness ’ order. For example, our method tolerates indirect paths between, say, the ‘CEO ’ and the ‘Accountant ’ of the above sample query, when direct paths do not exist. Its second feature is scalability. In general, if the query has nq nodes and the data graph has n nodes, the problem needs polynomial time complexity O(n nq), which is prohibitive. Our G-Ray (“Graph X-Ray”) method finds high-quality subgraphs in time linear on the size of the data graph. Experimental results on the DLBP author-publication graph (with 356K nodes and 1.9M edges) illustrate both the effectiveness and scalability of our approach. The results agree with our intuition, and the speed is excellent. It takes 4 seconds on average for a 4node query on the DBLP graph.

Citations