Capturing Topology in Graph Pattern Matching

TL;DR

This paper introduces strong simulation, a new graph pattern matching method that preserves graph topology, operates in cubic time, and is effective for distributed and large-scale graphs.

Contribution

It proposes strong simulation for topology-preserving graph pattern matching, with a cubic-time algorithm and proven effectiveness on real and synthetic data.

Findings

Strong simulation preserves graph topology.

It operates in cubic time, matching previous extensions.

Effective on distributed and large-scale graphs.

Abstract

Graph pattern matching is often defined in terms of subgraph isomorphism, an NP-complete problem. To lower its complexity, various extensions of graph simulation have been considered instead. These extensions allow pattern matching to be conducted in cubic-time. However, they fall short of capturing the topology of data graphs, i.e., graphs may have a structure drastically different from pattern graphs they match, and the matches found are often too large to understand and analyze. To rectify these problems, this paper proposes a notion of strong simulation, a revision of graph simulation, for graph pattern matching. (1) We identify a set of criteria for preserving the topology of graphs matched. We show that strong simulation preserves the topology of data graphs and finds a bounded number of matches. (2) We show that strong simulation retains the same complexity as earlier extensions of simulation, by providing a cubic-time algorithm for computing strong simulation. (3) We present the locality property of strong simulation, which allows us to effectively conduct pattern matching on distributed graphs. (4) We experimentally verify the effectiveness and efficiency of these algorithms, using real-life data and synthetic data.