Comparing Graph Clusterings: Set partition measures vs. Graph-aware measures

06/29/2018
by   Valérie Poulin, et al.
0

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 graph partitions. 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 assess that two graph partitions are similar.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/21/2018

How to sample connected K-partitions of a graph

A connected undirected graph G=(V,E) is given. This paper presents an al...
research
10/01/2020

A survey of mass partitions

Mass partition problems describe the partitions we can induce on a famil...
research
06/27/2012

A Split-Merge Framework for Comparing Clusterings

Clustering evaluation measures are frequently used to evaluate the perfo...
research
02/13/2017

On Seeking Consensus Between Document Similarity Measures

This paper investigates the application of consensus clustering and meta...
research
02/01/2021

Characterizing and comparing external measures for the assessment of cluster analysis and community detection

In the context of cluster analysis and graph partitioning, many external...
research
08/24/2020

Approximate Partition Selection for Big-Data Workloads using Summary Statistics

Many big-data clusters store data in large partitions that support acces...
research
05/16/2021

Lexicographic Enumeration of Set Partitions

In this report, we summarize the set partition enumeration problems and ...

Please sign up or login with your details

Forgot password? Click here to reset