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Reseach Article

A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint

by David F. Nettleton, Anton Dries
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 10
Year of Publication: 2015
Authors: David F. Nettleton, Anton Dries

David F. Nettleton, Anton Dries . A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint. International Journal of Computer Applications. 130, 10 ( November 2015), 29-38. DOI=10.5120/ijca2015907098

@article{ 10.5120/ijca2015907098,
author = { David F. Nettleton, Anton Dries },
title = { A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 10 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 29-38 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2015907098 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:25:03.571618+05:30
%A David F. Nettleton
%A Anton Dries
%T A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 10
%P 29-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Approximate sub-graph matching is important in many graph data mining fields. At present, current solutions can be difficult to implement, have an expensive pre-processing phase, or only work for given types of graph. In this paper a novel generic approach is presented which addresses these issues. An approximate sub-graph matcher (A-SGM) calculates the distance between the topological characteristics (footprint) of the sub-graphs to be matched, applying a weighting to the different sub-graph characteristics and those of neighbor nodes. The weights are calibrated for each dataset with a simulated annealing process using sample sets of graph nodes to reduce computational cost, and an exact isomorphism matcher as a fitness function which takes into account how well the match maintains the neighboring node degree distributions. Benchmarking is performed on several state of the art methods and real and synthetic graph datasets to evaluate the precision, recall and computational cost. The results show that the A-SGM is competitive with state of the art methods in terms of precision, recall and execution time.

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Index Terms

Computer Science
Information Sciences


Graph Matching topology graph characteristics weight calibration simulated annealing graph queries.