Measuring Distances Among Graphs En Route To Graph Clustering

Kyosev, I., Paun, I., Moshfeghi, Y. and Ntarmos, N. (2020) Measuring Distances Among Graphs En Route To Graph Clustering. In: 5th IEEE Workshop on Advances in High-Dimensional Big Data 2020, IEEE Big Data 2020, 10-13 Dec 2020, pp. 3632-3641. ISBN 9781728162515 (doi:10.1109/BigData50022.2020.9378333)

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The graph data structure offers a highly expressive way of representing many real-world constructs such as social networks, chemical compounds, the world wide web, street maps, etc. In essence, any collection of entities and the relationships between them can be modelled using a graph, thus preserving more information about the real-world objects than a simple vector space model. An issue that arises when operating on collections of graphs, however, is that most statistical analysis and machine learning methods expect their input data to be in the form of multidimensional vectors, where all items can be compared with each other using well-understood metrics such as Euclidean or Manhattan distance. This paper presents a variety of approaches for computing distances between graphs with known node correspondence, with the aim of applying those measures alongside clustering algorithms to discover patterns in a given dataset. The performance of each distance measure is then evaluated through its ability to identify communities of graphs with similar features. We show that because the considered distance metrics highlight different structural properties, the method that produces the highest quality result will depend on the characteristics of the processed graph population.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Ntarmos, Dr Nikos and Moshfeghi, Dr Yashar and Paun, Ms Iulia
Authors: Kyosev, I., Paun, I., Moshfeghi, Y., and Ntarmos, N.
College/School:College of Science and Engineering > School of Computing Science
Published Online:19 March 2021
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in 2020 IEEE International Conference on Big Data (Big Data)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172865EPSRC DTP 16/17 and 17/18Tania GalabovaEngineering and Physical Sciences Research Council (EPSRC)EP/N509668/1Research and Innovation Services