Combining topological and topical features for community detection

Retnani Latifah, Mirna Adriani

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

Abstract

Community detection is an important approach to identify community's structure in a network and can also be considered as graph clustering. This paper conducted a research about community detection using combined topological and topical features in Twitter. The combined features were compared to topological only and topical only. The topological features that were used are following-follower relationship and retweet-favorite ratio while topical features are hashtags, mentions, links and tweets. This research proposed a new node weight using retweet-favorite ratio to build topological matrix and it has been proved to have higher purity value by 30-40% and higher rand index value by 10-20%. The purity value of combining topological and topical features is also improved by 30% compared to using following-follower relationship as topological features. The highest rand index and purity values are achieved by matrix of combinied topological and topical features with multilevel community detection as clustering algorithm with 0.89 and 0.77.

Original languageEnglish
Title of host publication2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages327-334
Number of pages8
ISBN (Electronic)9781509046294
DOIs
Publication statusPublished - 6 Mar 2017
Event8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 - Malang, Indonesia
Duration: 15 Oct 201616 Oct 2016

Publication series

Name2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

Conference

Conference8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
Country/TerritoryIndonesia
CityMalang
Period15/10/1616/10/16

Keywords

  • community detection
  • graph clustering
  • node weight
  • topical
  • topological
  • twitter

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