GTST: A Python Package for Graph Two-Sample Testing

Ragnar L. Gudmundarson*, Gareth W. Peters

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

The GTST package is a python package for performing graph sample testing. The test infers whether two samples of graphs were generated from the same probability distribution or not. It is a very general framework as it allows comparison between binary, weighted, directed, node-labelled, node attributed and edge-labelled graphs. Up until now, there is no package which offers graph sample testing even though the problem is often encountered in various fields such as risk management, social sciences and molecular science. The flexibility of the test comes from so-called graph kernels which allow one to measure similarities between complex graph data. The difference between the two samples is quantified using an empirical estimate of the maximum mean discrepancy which is a distance on the space of probability measures. Along with testing of graph samples, the package offers various graph kernels, some of which have not been readily available before.

Original languageEnglish
Article number6
JournalJournal of Open Research Software
Volume12
DOIs
Publication statusPublished - 18 Mar 2024

Keywords

  • graph kernel
  • graph two sample testing
  • kernel
  • MMD

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Library and Information Sciences

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