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Combining bio-inspired meta-heuristics and novelty search for community detection over evolving graph streams

Published: 13 July 2019 Publication History

Abstract

Finding communities of interrelated nodes is a learning task that often holds in problems that can be modeled as a graph. In any case, detecting an optimal partition in a graph is highly time-consuming and complex. For this reason, the implementation of search-based metaheuristics arises as an alternative for addressing these problems. This manuscript focuses on optimally partitioning dynamic network instances, in which the connections between vertices change dynamically along time. Specifically, the application of Novelty Search mechanism for solving the problem of finding communities in dynamic networks is studied in this paper. For this goal, this procedure has been embedded in the search process undertaken by three different bio-inspired meta-heuristic schemes: Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization. All these methods have been properly adapted for dealing with this discrete and dynamic problem, using a reformulated expression of the modularity coefficient as its fitness function. A thorough experimentation has been conducted using a benchmark composed by 12 synthetically created instances, with the main objective of analyzing the performance of the proposed Novelty Search mechanism when facing this problem. In light of the outperforming behavior of our approach and its relevance dictated by two different statistical tests, we conclude that Novelty Search is a promising procedure for finding communities in evolving graph data.

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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 July 2019

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Author Tags

  1. bio-inspired computation
  2. community detection
  3. evolving graphic streams
  4. novelty search

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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  • (2021)A Review on Ensemble Methods and their Applications to Optimization ProblemsApplied Optimization and Swarm Intelligence10.1007/978-981-16-0662-5_2(25-45)Online publication date: 18-May-2021
  • (2020)An adaptive hybrid algorithm for social networks to choose groups with independent membersEvolutionary Intelligence10.1007/s12065-020-00384-xOnline publication date: 19-Mar-2020
  • (2020)Exploring Multi-objective Cellular Genetic Algorithms in Community Detection ProblemsIntelligent Data Engineering and Automated Learning – IDEAL 202010.1007/978-3-030-62365-4_22(223-235)Online publication date: 27-Oct-2020
  • (2019)Introductory Chapter: Swarm Intelligence - Recent Advances, New Perspectives, and ApplicationsSwarm Intelligence - Recent Advances, New Perspectives and Applications10.5772/intechopen.90066Online publication date: 4-Dec-2019
  • (2019)A Distributed Hybrid Community Detection Methodology for Social NetworksAlgorithms10.3390/a1208017512:8(175)Online publication date: 17-Aug-2019

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