主讲人：张忠元（中央财经大学教授）

邀请人：卓新建

时  间：2018年9月18日(周二) 9:30-10:30

地  点：主楼1214

报告摘要：

Community structures are critical towards understanding not only the network topology but also how the network functions. However, how to evaluate the quality of detected community structures is still challenging and remains unsolved. The most widely used metric, normalized mutual information (NMI), was proved to have finite size effect, and its improved form rNMI has reverse finite size effect. cNMI is thus proposed and has neither finite size effect nor reverse finite size effect. However, in this paper we show that cNMI violates the so-called proportionality assumption. In addition, NMI-type metrics have the problem of ignoring importance of small communities. Finally, they cannot be used to evaluate a single community of interest. In this paper, we map the computed community labels to the ground-truth ones through integer linear programming, then use Kappa index and F-score to evaluate the detected community structures. Experimental results demonstrate the rationality of our method.

报告人简介：

张忠元，理学博士，中央财经大学统计与数学学院教授，博士生导师，中国计算机学会高级会员，果壳网科学顾问。主要研究兴趣在复杂网络分析和智慧城市研究，在Data Mining and Knowledge Discovery，Physical Review E，EPL，Knowledge and Information Systems，Scientific Reports，PLOS ONE以及《中国科学》等国内外著名期刊上发表学术论文十余篇。爱思唯尔杰出审稿人, 担任Data Mining and Knowledge Discovery, Physica A, Management Science等著名期刊的匿名审稿人。