Particle competition and cooperation for uncovering network overlap community structure

Fabricio Breve; Liang Zhao; Marcos Quiles; Witold Pedrycz; Jiming LIU

doi:10.1007/978-3-642-21111-9_48

Particle competition and cooperation for uncovering network overlap community structure

Fabricio Breve^*, Liang Zhao, Marcos Quiles, Witold Pedrycz, Jiming LIU

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

4 Citations (Scopus)

Abstract

Identification and classification of overlap nodes in communities is an important topic in data mining. In this paper, a new graph-based (network-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in the network to uncover overlap nodes, i.e., the algorithm can output continuous-valued output (soft labels), which corresponds to the levels of membership from the nodes to each of the communities. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Original language	English
Title of host publication	Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011
Pages	426-433
Number of pages	8
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-21111-9_48
Publication status	Published - 2011
Event	8th International Symposium on Neural Networks, ISNN 2011 - Guilin, China Duration: 29 May 2011 → 1 Jun 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	6677 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Symposium on Neural Networks, ISNN 2011
Country/Territory	China
City	Guilin
Period	29/05/11 → 1/06/11

Scopus Subject Areas

Theoretical Computer Science
General Computer Science

User-Defined Keywords

community detection
Graph-based method
overlap nodes
particle competition and cooperation

Access to Document

10.1007/978-3-642-21111-9_48

Cite this

Breve, F., Zhao, L., Quiles, M., Pedrycz, W., & LIU, J. (2011). Particle competition and cooperation for uncovering network overlap community structure. In Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011 (PART 3 ed., pp. 426-433). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6677 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-21111-9_48

Breve, Fabricio ; Zhao, Liang ; Quiles, Marcos et al. / Particle competition and cooperation for uncovering network overlap community structure. Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 3. ed. 2011. pp. 426-433 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

@inproceedings{77a5f78e63f7470b844b9553fc8636d9,

title = "Particle competition and cooperation for uncovering network overlap community structure",

abstract = "Identification and classification of overlap nodes in communities is an important topic in data mining. In this paper, a new graph-based (network-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in the network to uncover overlap nodes, i.e., the algorithm can output continuous-valued output (soft labels), which corresponds to the levels of membership from the nodes to each of the communities. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.",

keywords = "community detection, Graph-based method, overlap nodes, particle competition and cooperation",

author = "Fabricio Breve and Liang Zhao and Marcos Quiles and Witold Pedrycz and Jiming LIU",

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doi = "10.1007/978-3-642-21111-9_48",

language = "English",

isbn = "9783642211102",

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

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edition = "PART 3",

}

Breve, F, Zhao, L, Quiles, M, Pedrycz, W & LIU, J 2011, Particle competition and cooperation for uncovering network overlap community structure. in Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6677 LNCS, pp. 426-433, 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, 29/05/11. https://doi.org/10.1007/978-3-642-21111-9_48

Particle competition and cooperation for uncovering network overlap community structure. / Breve, Fabricio; Zhao, Liang; Quiles, Marcos et al.
Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 3. ed. 2011. p. 426-433 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6677 LNCS, No. PART 3).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Particle competition and cooperation for uncovering network overlap community structure

AU - Breve, Fabricio

AU - Zhao, Liang

AU - Quiles, Marcos

AU - Pedrycz, Witold

AU - LIU, Jiming

PY - 2011

Y1 - 2011

N2 - Identification and classification of overlap nodes in communities is an important topic in data mining. In this paper, a new graph-based (network-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in the network to uncover overlap nodes, i.e., the algorithm can output continuous-valued output (soft labels), which corresponds to the levels of membership from the nodes to each of the communities. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

AB - Identification and classification of overlap nodes in communities is an important topic in data mining. In this paper, a new graph-based (network-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in the network to uncover overlap nodes, i.e., the algorithm can output continuous-valued output (soft labels), which corresponds to the levels of membership from the nodes to each of the communities. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

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KW - Graph-based method

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DO - 10.1007/978-3-642-21111-9_48

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011

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Breve F, Zhao L, Quiles M, Pedrycz W, LIU J. Particle competition and cooperation for uncovering network overlap community structure. In Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 3 ed. 2011. p. 426-433. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-21111-9_48

Particle competition and cooperation for uncovering network overlap community structure

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