Core-periphery clustering and collaboration networks

Pierluigi Crescenzi; Pierre Fraigniaud; Zvi Lotker; Paolo Penna

doi:10.1109/ASONAM.2016.7752285

Core-periphery clustering and collaboration networks

Pierluigi Crescenzi, Pierre Fraigniaud, Zvi Lotker, Paolo Penna

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

Abstract

In this paper we analyse the core-periphery clustering properties of collaboration networks, where the core of a network is formed by the nodes with highest degree. In particular, we first observe that, even for random graph models aiming at matching the degree-distribution and/or the clustering coefficient of real networks, these models produce synthetic graphs which have a spatial distribution of the triangles with respect to the core and to the periphery which does not match the spatial distribution of the triangles in the real networks. We therefore propose a new model, called CPCL, whose aim is to distribute the triangles in a way fitting with their real core-periphery distribution, and thus producing graphs matching the core-periphery clustering of real networks.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Editors	Ravi Kumar, James Caverlee, Hanghang Tong
Publisher	Institute of Electrical and Electronics Engineers
Pages	525-528
Number of pages	4
ISBN (Electronic)	9781509028467
DOIs	https://doi.org/10.1109/ASONAM.2016.7752285
State	Published - 21 Nov 2016
Event	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States Duration: 18 Aug 2016 → 21 Aug 2016

Publication series

Name	Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Conference

Conference	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Country/Territory	United States
City	San Francisco
Period	18/08/16 → 21/08/16

ASJC Scopus subject areas

Computer Networks and Communications
Sociology and Political Science
Communication

Access to Document

10.1109/ASONAM.2016.7752285

Cite this

Crescenzi, P., Fraigniaud, P., Lotker, Z., & Penna, P. (2016). Core-periphery clustering and collaboration networks. In R. Kumar, J. Caverlee, & H. Tong (Eds.), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 525-528). Article 7752285 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ASONAM.2016.7752285

Crescenzi, Pierluigi ; Fraigniaud, Pierre ; Lotker, Zvi et al. / Core-periphery clustering and collaboration networks. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. editor / Ravi Kumar ; James Caverlee ; Hanghang Tong. Institute of Electrical and Electronics Engineers, 2016. pp. 525-528 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

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title = "Core-periphery clustering and collaboration networks",

abstract = "In this paper we analyse the core-periphery clustering properties of collaboration networks, where the core of a network is formed by the nodes with highest degree. In particular, we first observe that, even for random graph models aiming at matching the degree-distribution and/or the clustering coefficient of real networks, these models produce synthetic graphs which have a spatial distribution of the triangles with respect to the core and to the periphery which does not match the spatial distribution of the triangles in the real networks. We therefore propose a new model, called CPCL, whose aim is to distribute the triangles in a way fitting with their real core-periphery distribution, and thus producing graphs matching the core-periphery clustering of real networks.",

author = "Pierluigi Crescenzi and Pierre Fraigniaud and Zvi Lotker and Paolo Penna",

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year = "2016",

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doi = "10.1109/ASONAM.2016.7752285",

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series = "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016",

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Crescenzi, P, Fraigniaud, P, Lotker, Z & Penna, P 2016, Core-periphery clustering and collaboration networks. in R Kumar, J Caverlee & H Tong (eds), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752285, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, Institute of Electrical and Electronics Engineers, pp. 525-528, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 18/08/16. https://doi.org/10.1109/ASONAM.2016.7752285

Core-periphery clustering and collaboration networks. / Crescenzi, Pierluigi; Fraigniaud, Pierre; Lotker, Zvi et al.
Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. ed. / Ravi Kumar; James Caverlee; Hanghang Tong. Institute of Electrical and Electronics Engineers, 2016. p. 525-528 7752285 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

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

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N2 - In this paper we analyse the core-periphery clustering properties of collaboration networks, where the core of a network is formed by the nodes with highest degree. In particular, we first observe that, even for random graph models aiming at matching the degree-distribution and/or the clustering coefficient of real networks, these models produce synthetic graphs which have a spatial distribution of the triangles with respect to the core and to the periphery which does not match the spatial distribution of the triangles in the real networks. We therefore propose a new model, called CPCL, whose aim is to distribute the triangles in a way fitting with their real core-periphery distribution, and thus producing graphs matching the core-periphery clustering of real networks.

AB - In this paper we analyse the core-periphery clustering properties of collaboration networks, where the core of a network is formed by the nodes with highest degree. In particular, we first observe that, even for random graph models aiming at matching the degree-distribution and/or the clustering coefficient of real networks, these models produce synthetic graphs which have a spatial distribution of the triangles with respect to the core and to the periphery which does not match the spatial distribution of the triangles in the real networks. We therefore propose a new model, called CPCL, whose aim is to distribute the triangles in a way fitting with their real core-periphery distribution, and thus producing graphs matching the core-periphery clustering of real networks.

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Crescenzi P, Fraigniaud P, Lotker Z, Penna P. Core-periphery clustering and collaboration networks. In Kumar R, Caverlee J, Tong H, editors, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers. 2016. p. 525-528. 7752285. (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). doi: 10.1109/ASONAM.2016.7752285

Core-periphery clustering and collaboration networks

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