Towards large-scale social networks with online diffusion provenance detection

Wang, H; Wu, J; Pan, S; Zhang, P; Chen, L

Towards large-scale social networks with online diffusion provenance detection

Wang, H Wu, J

Pan, S

Zhang, P

Chen, L

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Publication Type:: Journal Article
Citation:: Computer Networks, 2017, 114 pp. 154 - 166
Issue Date:: 2017-02-26

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Field	Value	Language
dc.contributor.author	Wang, H	en_US
dc.contributor.author	Wu, J https://orcid.org/0000-0002-1371-5801	en_US
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729	en_US
dc.date.available	2020-05-25T19:05:04Z
dc.date.issued	2017-02-26	en_US
dc.identifier.citation	Computer Networks, 2017, 114 pp. 154 - 166	en_US
dc.identifier.issn	1389-1286	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124301
dc.description.abstract	© 2016 Elsevier B.V. In this paper we study a new problem of online discovering diffusion provenances in large networks. Existing work on network diffusion provenance identification focuses on offline learning where data collected from network detectors are static and a snapshot of the network is available before learning. However, an offline learning model does not meet the need for early warning, real-time awareness, or a real-time response to malicious information spreading in networks. To this end, we propose an online regression model for real-time diffusion provenance identification. Specifically, we first use offline collected network cascades to infer the edge transmission weights, and then use an online l1 non-convex regression model as the identification model. The proposed methods are empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140100545
dc.relation	http://purl.org/au-research/grants/arc/DP140102206
dc.relation.ispartof	Computer Networks	en_US
dc.relation.isbasedon	10.1016/j.comnet.2016.08.025	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Towards large-scale social networks with online diffusion provenance detection	en_US
dc.type	Journal Article
utslib.citation.volume	114	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	114	en_US

Abstract:

© 2016 Elsevier B.V. In this paper we study a new problem of online discovering diffusion provenances in large networks. Existing work on network diffusion provenance identification focuses on offline learning where data collected from network detectors are static and a snapshot of the network is available before learning. However, an offline learning model does not meet the need for early warning, real-time awareness, or a real-time response to malicious information spreading in networks. To this end, we propose an online regression model for real-time diffusion provenance identification. Specifically, we first use offline collected network cascades to infer the edge transmission weights, and then use an online l1 non-convex regression model as the identification model. The proposed methods are empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.

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http://hdl.handle.net/10453/124301