A Hybrid Approach to Temporal Pattern Matching
This addresses the challenge of temporal pattern matching for researchers and practitioners working with graph data, but it appears incremental as it builds on existing graph pattern matching methods.
They tackled the problem of finding matches of interaction patterns in temporal graphs by proposing a hybrid approach that filters potential matches based on structure and time, achieving efficiency as demonstrated in experiments with real datasets.
The primary objective of graph pattern matching is to find all appearances of an input graph pattern query in a large data graph. Such appearances are called matches. In this paper, we are interested in finding matches of interaction patterns in 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.