A Hybrid Approach to Temporal Pattern Matching

Abstract

Temporal graphs represent relationships and interactions among entities over time, such as those occurring among users in social, transaction, and telecommunication networks. The analysis of their temporal structure help us understand, and predict the behavior of their entities. A typical analysis task in graph networks is the finding of all appearances of an input graph pattern query. Such appearances are called matches. In this paper, we are interested in finding all matches of an interaction pattern query within temporal graphs. To this end, we propose a hybrid approach that achieves effective filtering of potential matches based both on structure and time. Our approach exploits a graph representation where edges are ordered by time. We present experiments with real datasets that illustrate the efficiency of our approach.