Leveraging multi-level dependency of relational sequences for social spammer detection

Yin, J; Li, Q; Liu, S; Wu, Z; Xu, G

Leveraging multi-level dependency of relational sequences for social spammer detection

Yin, J Li, Q Liu, S Wu, Z Xu, G

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Neurocomputing, 2021, 428, pp. 130-141
Issue Date:: 2021-03-07

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Field	Value	Language
dc.contributor.author	Yin, J
dc.contributor.author	Li, Q
dc.contributor.author	Liu, S
dc.contributor.author	Wu, Z
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date.accessioned	2021-01-13T01:48:44Z
dc.date.available	2021-01-13T01:48:44Z
dc.date.issued	2021-03-07
dc.identifier.citation	Neurocomputing, 2021, 428, pp. 130-141
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/145361
dc.description.abstract	© 2020 Elsevier B.V. Much recent research has shed light on developing the relation-dependent but the content-independent framework for social spammer detection. This is mainly because the relation among users is difficult to be altered when spammers attempt to conceal their malicious intentions. Our study investigates the spammer detection problem in the context of multi-relation social networks and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy. Specifically, we present the Multi-level Dependency Model (MDM). The MDM is able to exploit the user's long-term dependency hidden in their relational sequences along with short-term dependency. Moreover, MDM fully considers short-term relational sequences from the perspectives of individual-level and union-level, due to the fact that the type of short-term sequences is multi-folds. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM on multi-relational social spammer detection.
dc.language	en
dc.publisher	Elsevier BV
dc.relation	http://purl.org/au-research/grants/arc/LP170100891
dc.relation	http://purl.org/au-research/grants/arc/DP200101374
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2020.10.070
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Leveraging multi-level dependency of relational sequences for social spammer detection
dc.type	Journal Article
utslib.citation.volume	428
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
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
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-03-07T00:00:00+1000Z
dc.date.updated	2021-01-13T01:48:41Z
pubs.publication-status	Accepted
pubs.volume	428

Abstract:

© 2020 Elsevier B.V. Much recent research has shed light on developing the relation-dependent but the content-independent framework for social spammer detection. This is mainly because the relation among users is difficult to be altered when spammers attempt to conceal their malicious intentions. Our study investigates the spammer detection problem in the context of multi-relation social networks and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy. Specifically, we present the Multi-level Dependency Model (MDM). The MDM is able to exploit the user's long-term dependency hidden in their relational sequences along with short-term dependency. Moreover, MDM fully considers short-term relational sequences from the perspectives of individual-level and union-level, due to the fact that the type of short-term sequences is multi-folds. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM on multi-relational social spammer detection.

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http://hdl.handle.net/10453/145361