Analysing Ego-Networks via Typed-Edge Graphlets: A Case Study of Chronic Pain Patients

Jia, M; Van Alboom, M; Goubert, L; Bracke, P; Gabrys, B; Musial, K

Analysing Ego-Networks via Typed-Edge Graphlets: A Case Study of Chronic Pain Patients

Jia, M Van Alboom, M Goubert, L Bracke, P Gabrys, B

Musial, K

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Publisher:: Springer International Publishing
Publication Type:: Chapter
Citation:: Complex Networks & Their Applications X, 2022, 1015, pp. 514-526
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Jia, M
dc.contributor.author	Van Alboom, M
dc.contributor.author	Goubert, L
dc.contributor.author	Bracke, P
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.contributor.author	Musial, K
dc.date.accessioned	2022-08-06T00:36:52Z
dc.date.available	2022-08-06T00:36:52Z
dc.date.issued	2022-01-01
dc.identifier.citation	Complex Networks & Their Applications X, 2022, 1015, pp. 514-526
dc.identifier.isbn	9783030934088
dc.identifier.uri	http://hdl.handle.net/10453/159690
dc.description.abstract	Graphlets, being the fundamental building blocks, are essential for understanding and analysing complex networks. The original notion of graphlets, however, is unable to encode edge attributes in many types of networks, especially in egocentric social networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Through applying the proposed method to a case study of chronic pain patients, we find that not only a patient’s social network structure could inform his/her perceived pain grade, but also particular types of social relationships, such as friends, colleagues and healthcare workers, are more important in understanding the effect of chronic pain. Further, we demonstrate that including TyE-GDV as additional features leads to significant improvement in a typical machine learning task.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation	http://purl.org/au-research/grants/arc/DP190101087
dc.relation.ispartof	Complex Networks & Their Applications X
dc.relation.isbasedon	10.1007/978-3-030-93409-5_43
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Analysing Ego-Networks via Typed-Edge Graphlets: A Case Study of Chronic Pain Patients
dc.type	Chapter
utslib.citation.volume	1015
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-01-01T00:00:00+1000Z
dc.date.updated	2022-08-06T00:36:51Z
pubs.publication-status	Published
pubs.volume	1015

Abstract:

Graphlets, being the fundamental building blocks, are essential for understanding and analysing complex networks. The original notion of graphlets, however, is unable to encode edge attributes in many types of networks, especially in egocentric social networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Through applying the proposed method to a case study of chronic pain patients, we find that not only a patient’s social network structure could inform his/her perceived pain grade, but also particular types of social relationships, such as friends, colleagues and healthcare workers, are more important in understanding the effect of chronic pain. Further, we demonstrate that including TyE-GDV as additional features leads to significant improvement in a typical machine learning task.

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http://hdl.handle.net/10453/159690