Համահեղինակ(ներ):

Mkhitaryan Karen ; Mothe Josiane

Ամփոփում:

Community detection is a research area from network science dealing with the investigation of complex networks such as biological, social and computer networks, aiming to identify subgroups (communities) of entities (nodes) that are more closely related to each other than with remaining entities in the network [1]. Various community detection algorithms are used in the literature however the evaluation of their derived community structure is a challenging task due to varying results on different networks. In searching good community detection algorithms diverse comparison measures are used actively [2]. Information theoretic measures form a fundamental class in this discipline and have recently received increasing interest [3]. In this paper we first mention the usual evaluation measures used for community detection evaluation We then review the properties of f-divergence measures and propose the ones that can serve community detection evaluation. Preliminary experiments show the advantage of these measures in the case of large number of communities.

Նույնականացուցիչ:

oai:noad.sci.am:135852

Լեզու:

English

URL:

Կազմակերպության անվանում:

Institute for Informatics and Automation Problems ; IRIT, UMR5505 CNRS & ESPE, Univ. de Toulouse,

Երկիր:

Armenia

Տարի:

2018

Ժամանակահատված:

September 12-15

Գիտաժողովի անվանում:

Collaborative Technologies and Data science in Smart City Applications

Վայր:

Yerevan

Մասնակցության տեսակը:

oral