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

作者信息

Poulin Valerie, Theberge Francois

出版信息

IEEE Trans Pattern Anal Mach Intell. 2021 Jun;43(6):2127-2132. doi: 10.1109/TPAMI.2020.3009862. Epub 2021 May 11.

DOI:10.1109/TPAMI.2020.3009862
PMID:32750819
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.

摘要

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