A customizable inexact subgraph matching algorithm for attributed graphs

TL;DR

This paper introduces a flexible, customizable inexact subgraph matching algorithm that leverages node and edge attributes with a modifiable graph edit distance to identify patterns in attributed graphs.

Contribution

The authors present a novel inexact subgraph matching algorithm that uses attribute information and a customizable cost function for broader applicability.

Findings

Effective on family trees and control-flow graphs.

Handles noisy and error-prone datasets.

Flexible in the type of subgraph matching performed.

Abstract

Graphs provide a natural way to represent data by encoding information about objects and the relationships between them. With the ever-increasing amount of data collected and generated, locating specific patterns of relationships between objects in a graph is often required. Given a larger graph and a smaller graph, one may wish to identify instances of the smaller query graph in the larger target graph. This task is called subgraph identification or matching. Subgraph matching is helpful in areas such as bioinformatics, binary analysis, pattern recognition, and computer vision. In these applications, datasets frequently contain noise and errors, thus exact subgraph matching algorithms do not apply. In this paper we introduce a new customizable algorithm for inexact subgraph matching. Our algorithm utilizes node and edge attributes which are often present in real-world datasets to narrow down the search space. The algorithm is flexible in the type of subgraph matching it can perform and the types of datasets it can process by its use of a modifiable graph edit distance cost function for pairing nodes. We show its effectiveness on family trees graphs and control-flow graphs.