Rough Net Approach for Community Detection Analysis in Complex Networks

Ivett Elena Fuentes Herrera; Arian Pina; Gonzalo Nápoles; Leticia Arco; Koen Vanhoof

doi:10.1007/978-3-030-52705-1_30

Rough Net Approach for Community Detection Analysis in Complex Networks

Ivett Elena Fuentes Herrera, Arian Pina, Gonzalo Nápoles, Leticia Arco, Koen Vanhoof

Informatics and Applied Informatics

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

3 Citations (Scopus)

Abstract

Rough set theory has many interesting applications in circumstances characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis are discussed based on the Rough Net definition. We will focus the application of Rough Net on community detection validity in both monoplex and multiplex networks. Also, the topological evolution estimation between adjacent layers in dynamic networks is discussed and a new community interaction visualization approach combining both complex network representation and Rough Net definition is adopted to interpret the community structure. We provide some examples that illustrate how the Rough Net definition can be used to analyze the properties of the community structure in real-world networks, including dynamic networks.

Original language	English
Title of host publication	Rough Sets
Subtitle of host publication	Proceedings of the International Joint Conference (IJCRS 2020), Havana, Cuba, June 29 – July 3, 2020
Editors	Rafael Bello, Duoqian Miao, Rafael Falcón, Michinori Nakata, Alejandro Rosete, Davide Ciucci
Publisher	Springer Verlag
Pages	401–415
Number of pages <span style="color:red"p> <font size="1.5"> ✽ </span> </font>	15
Volume	12179
ISBN (Electronic)	978-3-030-52705-1
ISBN (Print)	978-3-030-52704-4
DOIs	https://doi.org/10.1007/978-3-030-52705-1_30
Publication status	Published - 7 Jul 2020
Event	International Joint Conference on Rough Sets - Melia Habana Hotel, Havana, Cuba Duration: 29 Jun 2020 → 3 Jul 2020 https://easychair.org/cfp/IJCRS2020

Publication series

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer
Volume	12179
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Joint Conference on Rough Sets
Abbreviated title	IJCRS 2020
Country/Territory	Cuba
City	Havana
Period	29/06/20 → 3/07/20
Internet address	https://easychair.org/cfp/IJCRS2020

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10.1007/978-3-030-52705-1_30

https://link.springer.com/chapter/10.1007/978-3-030-52705-1_30

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Fuentes Herrera, I. E., Pina, A., Nápoles, G., Arco, L., & Vanhoof, K. (2020). Rough Net Approach for Community Detection Analysis in Complex Networks. In R. Bello, D. Miao, R. Falcón, M. Nakata, A. Rosete, & D. Ciucci (Eds.), Rough Sets: Proceedings of the International Joint Conference (IJCRS 2020), Havana, Cuba, June 29 – July 3, 2020 (Vol. 12179, pp. 401–415). (Lecture Notes in Artificial Intelligence; Vol. 12179). Springer Verlag. https://doi.org/10.1007/978-3-030-52705-1_30

Fuentes Herrera, Ivett Elena ; Pina, Arian ; Nápoles, Gonzalo ; Arco, Leticia ; Vanhoof, Koen. / Rough Net Approach for Community Detection Analysis in Complex Networks. Rough Sets: Proceedings of the International Joint Conference (IJCRS 2020), Havana, Cuba, June 29 – July 3, 2020. editor / Rafael Bello ; Duoqian Miao ; Rafael Falcón ; Michinori Nakata ; Alejandro Rosete ; Davide Ciucci. Vol. 12179 Springer Verlag, 2020. pp. 401–415 (Lecture Notes in Artificial Intelligence).

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title = "Rough Net Approach for Community Detection Analysis in Complex Networks",

abstract = "Rough set theory has many interesting applications in circumstances characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis are discussed based on the Rough Net definition. We will focus the application of Rough Net on community detection validity in both monoplex and multiplex networks. Also, the topological evolution estimation between adjacent layers in dynamic networks is discussed and a new community interaction visualization approach combining both complex network representation and Rough Net definition is adopted to interpret the community structure. We provide some examples that illustrate how the Rough Net definition can be used to analyze the properties of the community structure in real-world networks, including dynamic networks.",

author = "{Fuentes Herrera}, {Ivett Elena} and Arian Pina and Gonzalo N{\'a}poles and Leticia Arco and Koen Vanhoof",

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booktitle = "Rough Sets",

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Fuentes Herrera, IE, Pina, A, Nápoles, G, Arco, L & Vanhoof, K 2020, Rough Net Approach for Community Detection Analysis in Complex Networks. in R Bello, D Miao, R Falcón, M Nakata, A Rosete & D Ciucci (eds), Rough Sets: Proceedings of the International Joint Conference (IJCRS 2020), Havana, Cuba, June 29 – July 3, 2020. vol. 12179, Lecture Notes in Artificial Intelligence, vol. 12179, Springer Verlag, pp. 401–415, International Joint Conference on Rough Sets, Havana, Cuba, 29/06/20. https://doi.org/10.1007/978-3-030-52705-1_30

Rough Net Approach for Community Detection Analysis in Complex Networks. / Fuentes Herrera, Ivett Elena; Pina, Arian; Nápoles, Gonzalo; Arco, Leticia; Vanhoof, Koen.

Rough Sets: Proceedings of the International Joint Conference (IJCRS 2020), Havana, Cuba, June 29 – July 3, 2020. ed. / Rafael Bello; Duoqian Miao; Rafael Falcón; Michinori Nakata; Alejandro Rosete; Davide Ciucci. Vol. 12179 Springer Verlag, 2020. p. 401–415 (Lecture Notes in Artificial Intelligence; Vol. 12179).

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

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T1 - Rough Net Approach for Community Detection Analysis in Complex Networks

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AB - Rough set theory has many interesting applications in circumstances characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis are discussed based on the Rough Net definition. We will focus the application of Rough Net on community detection validity in both monoplex and multiplex networks. Also, the topological evolution estimation between adjacent layers in dynamic networks is discussed and a new community interaction visualization approach combining both complex network representation and Rough Net definition is adopted to interpret the community structure. We provide some examples that illustrate how the Rough Net definition can be used to analyze the properties of the community structure in real-world networks, including dynamic networks.

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Fuentes Herrera IE, Pina A, Nápoles G, Arco L, Vanhoof K. Rough Net Approach for Community Detection Analysis in Complex Networks. In Bello R, Miao D, Falcón R, Nakata M, Rosete A, Ciucci D, editors, Rough Sets: Proceedings of the International Joint Conference (IJCRS 2020), Havana, Cuba, June 29 – July 3, 2020. Vol. 12179. Springer Verlag. 2020. p. 401–415. (Lecture Notes in Artificial Intelligence). https://doi.org/10.1007/978-3-030-52705-1_30

Rough Net Approach for Community Detection Analysis in Complex Networks

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