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Comparing Graph Clusterings: Set Partition Measures vs. Graph-Aware Measures.

Authors :
Poulin, Valerie
Theberge, Francois
Source :
IEEE Transactions on Pattern Analysis & Machine Intelligence; Jun2021, Vol. 43 Issue 6, p2127-2132, 6p
Publication Year :
2021

Abstract

In this paper, we propose a family of graph partition similarity measures that take the topology of the graph into account. These graph-aware measures are alternatives to using set partition similarity measures that are not specifically designed for graphs. The two types of measures, graph-aware and set partition measures, are shown to have opposite behaviors with respect to resolution issues and provide complementary information necessary to compare graph partitions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01628828
Volume :
43
Issue :
6
Database :
Complementary Index
Journal :
IEEE Transactions on Pattern Analysis & Machine Intelligence
Publication Type :
Academic Journal
Accession number :
150287140
Full Text :
https://doi.org/10.1109/TPAMI.2020.3009862