Is Normalized Mutual Information a Fair Measure for Comparing Community Detection Methods?

被引：68

作者：

Amelio, Alessia ^{[1
]}

Pizzuti, Clara ^{[1
]}

机构：

[1] Natl Res Council Italy CNR, Inst High Perf Comp & Networking ICAR, Via Pietro Bucci 41C, I-87036 Arcavacata Di Rende, CS, Italy

来源：

PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015) | 2015年

关键词：

Normalized Mutual Information; community detection; selection bias;

D O I：

10.1145/2808797.2809344

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Normalized mutual information (NMI) is a widely used measure to compare community detection methods. Recently, however, the need of adjustment for information theoretic based measures has been argued because of their tendency in choosing clustering solutions with more communities. In this paper an experimental evaluation is performed to investigate this problem, and an adjustment that scales the values of NMI is proposed. Experiments on synthetic generated networks highlight the unbiased behavior of scaled NMI.

引用

页码：1584 / 1585

页数：2