Ants crawling to discover the community structure in networks

Mariano Tepper; Guillermo Sapiro

doi:10.1007/978-3-642-41827-3_69

Ants crawling to discover the community structure in networks

Mariano Tepper, Guillermo Sapiro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

We cast the problem of discovering the community structure in networks as the composition of community candidates, obtained from several community detection base algorithms, into a coherent structure. In turn, this composition can be cast into a maximum-weight clique problem, and we propose an ant colony optimization algorithm to solve it. Our results show that the proposed method is able to discover better community structures, according to several evaluation criteria, than the ones obtained with the base algorithms. It also outperforms, both in quality and in speed, the recently introduced FG-Tiling algorithm.

Original language	English (US)
Title of host publication	Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings
Pages	552-559
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-41827-3_69
State	Published - 2013
Externally published	Yes
Event	18th Iberoamerican Congress on Pattern Recognition, CIARP 2013 - Havana, Cuba Duration: Nov 20 2013 → Nov 23 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	8259 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th Iberoamerican Congress on Pattern Recognition, CIARP 2013
Country/Territory	Cuba
City	Havana
Period	11/20/13 → 11/23/13

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-41827-3_69

Cite this

Tepper, M., & Sapiro, G. (2013). Ants crawling to discover the community structure in networks. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings (PART 2 ed., pp. 552-559). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8259 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-41827-3_69

Tepper, Mariano ; Sapiro, Guillermo. / Ants crawling to discover the community structure in networks. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings. PART 2. ed. 2013. pp. 552-559 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Ants crawling to discover the community structure in networks",

abstract = "We cast the problem of discovering the community structure in networks as the composition of community candidates, obtained from several community detection base algorithms, into a coherent structure. In turn, this composition can be cast into a maximum-weight clique problem, and we propose an ant colony optimization algorithm to solve it. Our results show that the proposed method is able to discover better community structures, according to several evaluation criteria, than the ones obtained with the base algorithms. It also outperforms, both in quality and in speed, the recently introduced FG-Tiling algorithm.",

author = "Mariano Tepper and Guillermo Sapiro",

year = "2013",

doi = "10.1007/978-3-642-41827-3_69",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings",

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Tepper, M & Sapiro, G 2013, Ants crawling to discover the community structure in networks. in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8259 LNCS, pp. 552-559, 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013, Havana, Cuba, 11/20/13. https://doi.org/10.1007/978-3-642-41827-3_69

Ants crawling to discover the community structure in networks. / Tepper, Mariano; Sapiro, Guillermo.
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings. PART 2. ed. 2013. p. 552-559 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8259 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - We cast the problem of discovering the community structure in networks as the composition of community candidates, obtained from several community detection base algorithms, into a coherent structure. In turn, this composition can be cast into a maximum-weight clique problem, and we propose an ant colony optimization algorithm to solve it. Our results show that the proposed method is able to discover better community structures, according to several evaluation criteria, than the ones obtained with the base algorithms. It also outperforms, both in quality and in speed, the recently introduced FG-Tiling algorithm.

AB - We cast the problem of discovering the community structure in networks as the composition of community candidates, obtained from several community detection base algorithms, into a coherent structure. In turn, this composition can be cast into a maximum-weight clique problem, and we propose an ant colony optimization algorithm to solve it. Our results show that the proposed method is able to discover better community structures, according to several evaluation criteria, than the ones obtained with the base algorithms. It also outperforms, both in quality and in speed, the recently introduced FG-Tiling algorithm.

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Tepper M, Sapiro G. Ants crawling to discover the community structure in networks. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings. PART 2 ed. 2013. p. 552-559. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-41827-3_69

Ants crawling to discover the community structure in networks

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