Genetic algorithms for finding episodes in temporal networks

Mauro Castelli, Riccardo Dondi, Mohammad Mehdi Hosseinzadeh

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

1 Downloads (Pure)

Abstract

The evolution of networks is a fundamental topic in network analysis and mining. One of the approaches that has been recently considered in this field is the analysis of temporal networks, where relations between elements can change over time. A relevant problem in the analysis of temporal networks is the identification of cohesive or dense subgraphs since they are related to communities. In this contribution, we present a method based on genetic algorithms and on a greedy heuristic to identify dense subgraphs in a temporal network. We present experimental results considering both synthetic and real-networks, and we analyze the performance of the proposed method when varying the size of the population and the number of generations. The experimental results show that our heuristic generally performs better in terms of quality of the solutions than the state-of-art method for this problem. On the other hand, the state-of-art method is faster, although comparable with our method, when the size of the population and the number of generations are limited to small values.

Original languageEnglish
Title of host publicationKnowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020
EditorsMatteo Cristiani, Carlos Toro, Cecilia Zanni-Merk, Robert J. Howlett, Lakhmi C. Jain
PublisherElsevier
Pages215-224
Number of pages10
Volume176
DOIs
Publication statusPublished - 1 Oct 2020
Event24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020 - Virtual Online
Duration: 16 Sep 202018 Sep 2020

Publication series

NameProcedia Computer Science
PublisherElsevier
Volume176
ISSN (Print)1877-0509

Conference

Conference24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020
CityVirtual Online
Period16/09/2018/09/20

Keywords

  • Densest subgraph
  • Genetic algorithms
  • Network analysis and mining
  • Temporal networks

Fingerprint

Dive into the research topics of 'Genetic algorithms for finding episodes in temporal networks'. Together they form a unique fingerprint.

Cite this