Durable graph pattern queries on historical graphs

In this paper, we focus on labeled graphs that evolve over time. Given a sequence of graph snapshots representing the state of the graph at different time instants, we seek to find the most durable matches of an input graph pattern query, that is, the matches that exist for the longest period of time. The straightforward way to address this problem is by running a state-of-the-art graph pattern algorithm at each snapshot and aggregating the results. However, for large networks this approach is computationally expensive, since all matches have to be generated at each snapshot, including those appearing only once. We propose a new approach that uses a compact representation of the sequence of graph snapshots, appropriate time indexes to prune the search space and a threshold on the duration of the pattern to determine the search order. We also present experimental results using real datasets that illustrate the efficiency and effectiveness of our approach.