iGraphMatch: an R Package for the Analysis of Graph Matching

TL;DR

iGraphMatch is an R package that offers multiple algorithms and tools for effective graph matching, including handling prior information, visualization, and simulation, demonstrated through real-world network data examples.

Contribution

The paper introduces iGraphMatch, a comprehensive R package that integrates diverse graph matching algorithms, prior information incorporation, and visualization tools for practical network analysis.

Findings

Effective matching of various graph types demonstrated

Versatile incorporation of prior information shown

Successful application to real-world network data

Abstract

iGraphMatch is an R package for finding corresponding vertices between two graphs, also known as graph matching. The package implements three categories of prevalent graph matching algorithms including relaxation-based, percolation-based, and spectral-based, which are applicable to matching graphs under general settings: weighted directed graphs of different order and graphs of multiple layers. We provide versatile options to incorporate prior information in the form of seeds with or without noise and similarity scores. In addition, iGraphMatch provides functions to summarize the graph matching results in terms of several evaluation measures and visualize the matching performance. Finally, the package enables users to sample correlated random graph pairs from classic random graph models to generate data for simulations. This paper illustrates the practical applications of the package to the analysis of graph matching by detailed examples using real data from communication, neuron, and transportation networks.