Proximity, communities, and attributes in social network visualisation

Helen C. Purchase; Nathan Stirling; Daniel Archambault

doi:10.1109/ASONAM49781.2020.9381332

Proximity, communities, and attributes in social network visualisation

Helen C. Purchase, Nathan Stirling, Daniel Archambault

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

Abstract

The identification of groups in social networks drawn as graphs is an important task for social scientists who wish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the graph: That is, sets of people (nodes) where the relationships (edges) between them are more numerous than their relationships with other nodes. This approach to determining communities is naturally based on the underlying structure of the network, rather than on attributes associated with nodes. In this paper, we report on an experiment that (a) compares the effectiveness of several force-directed graph layout algorithms for visually identifying communities, and (b) investigates their usefulness when group membership is based not on structure, but on attributes associated with the people in the network. We find algorithms that clearly separate communities with large distances to be most effective, while using colour to represent community membership is more successful than reliance on structural layout.

Original language	English
Title of host publication	Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2020
Editors	Martin Atzmuller, Michele Coscia, Rokia Missaoui
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	65-72
Number of pages	8
ISBN (Electronic)	9781728110561
ISBN (Print)	9781728110578
DOIs	https://doi.org/10.1109/ASONAM49781.2020.9381332
Publication status	Published - 7 Dec 2020
Externally published	Yes
Event	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2020 - Online, Netherlands Duration: 7 Dec 2020 → 10 Dec 2020 Conference number: 12th https://ieeexplore.ieee.org/xpl/conhome/9381192/proceeding (Proceedings) https://asonam.cpsc.ucalgary.ca/2020/ (Website)

Publication series

Name	Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Publisher	IEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)	2473-9928
ISSN (Electronic)	2473-991X

Conference

Conference	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2020
Abbreviated title	ASONAM 2020
Country/Territory	Netherlands
Period	7/12/20 → 10/12/20
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9381192/proceeding (Proceedings) https://asonam.cpsc.ucalgary.ca/2020/ (Website)

Keywords

community detection
network layout
proximity
social networks

Access to Document

10.1109/ASONAM49781.2020.9381332

Cite this

Purchase, H. C., Stirling, N., & Archambault, D. (2020). Proximity, communities, and attributes in social network visualisation. In M. Atzmuller, M. Coscia, & R. Missaoui (Eds.), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2020 (pp. 65-72). (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ASONAM49781.2020.9381332

Purchase, Helen C. ; Stirling, Nathan ; Archambault, Daniel. / Proximity, communities, and attributes in social network visualisation. Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2020. editor / Martin Atzmuller ; Michele Coscia ; Rokia Missaoui. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2020. pp. 65-72 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

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title = "Proximity, communities, and attributes in social network visualisation",

abstract = "The identification of groups in social networks drawn as graphs is an important task for social scientists who wish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the graph: That is, sets of people (nodes) where the relationships (edges) between them are more numerous than their relationships with other nodes. This approach to determining communities is naturally based on the underlying structure of the network, rather than on attributes associated with nodes. In this paper, we report on an experiment that (a) compares the effectiveness of several force-directed graph layout algorithms for visually identifying communities, and (b) investigates their usefulness when group membership is based not on structure, but on attributes associated with the people in the network. We find algorithms that clearly separate communities with large distances to be most effective, while using colour to represent community membership is more successful than reliance on structural layout.",

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Purchase, HC, Stirling, N & Archambault, D 2020, Proximity, communities, and attributes in social network visualisation. in M Atzmuller, M Coscia & R Missaoui (eds), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2020. Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 65-72, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2020, Netherlands, 7/12/20. https://doi.org/10.1109/ASONAM49781.2020.9381332

Proximity, communities, and attributes in social network visualisation. / Purchase, Helen C.; Stirling, Nathan; Archambault, Daniel.
Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2020. ed. / Martin Atzmuller; Michele Coscia; Rokia Missaoui. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2020. p. 65-72 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

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

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N2 - The identification of groups in social networks drawn as graphs is an important task for social scientists who wish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the graph: That is, sets of people (nodes) where the relationships (edges) between them are more numerous than their relationships with other nodes. This approach to determining communities is naturally based on the underlying structure of the network, rather than on attributes associated with nodes. In this paper, we report on an experiment that (a) compares the effectiveness of several force-directed graph layout algorithms for visually identifying communities, and (b) investigates their usefulness when group membership is based not on structure, but on attributes associated with the people in the network. We find algorithms that clearly separate communities with large distances to be most effective, while using colour to represent community membership is more successful than reliance on structural layout.

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Purchase HC, Stirling N, Archambault D. Proximity, communities, and attributes in social network visualisation. In Atzmuller M, Coscia M, Missaoui R, editors, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2020. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2020. p. 65-72. (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). doi: 10.1109/ASONAM49781.2020.9381332

Proximity, communities, and attributes in social network visualisation

Abstract

Publication series

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Keywords

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