Community detection in networks

Santo Fortunato*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

The course is focused on one of the most popular topics in the network science: detection of communities in networks. Communities are usually conceived as subgraphs of a network, with a high density of links within the subgraphs and a comparatively lower density between them. I introduce the elements of the problem, e.g. definitions of community and partition, and dwelve into some of the most popular methods. Special attention is devoted to the optimization of global quality functions, like Newmna-Girvan modularity, and to their limits. Finally we discuss the crucial issue of testing, both on artificial benchmark graphs with built-in community structure and on real networks.

Original languageEnglish
Title of host publicationInformation Retrieval - 9th Russian Summer School, RuSSIR 2015, Revised Selected Papers
PublisherSpringer Verlag
Volume573
ISBN (Print)9783319417172
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventRussian Summer School in Information Retrieval - St. Petersburg, Russian Federation
Duration: 24 Aug 201528 Aug 2015
Conference number: 9

Publication series

NameCommunications in Computer and Information Science
Volume573
ISSN (Print)18650929

Seminar

SeminarRussian Summer School in Information Retrieval
Abbreviated titleRuSSIR
CountryRussian Federation
CitySt. Petersburg
Period24/08/201528/08/2015

Keywords

  • Community detection
  • Network science

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