Integrating ant colony algorithm and node centrality to improve prediction of information diffusion in social networks

Majbouri Yazdi, K; Yazdi, AM; Khodayi, S; Hou, J; Zhou, W; Saedy, S

Integrating ant colony algorithm and node centrality to improve prediction of information diffusion in social networks

Majbouri Yazdi, K Yazdi, AM Khodayi, S Hou, J Zhou, W

Saedy, S

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11342 LNCS pp. 381 - 391
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Majbouri Yazdi, K	en_US
dc.contributor.author	Yazdi, AM	en_US
dc.contributor.author	Khodayi, S	en_US
dc.contributor.author	Hou, J	en_US
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521	en_US
dc.contributor.author	Saedy, S	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11342 LNCS pp. 381 - 391	en_US
dc.identifier.isbn	9783030053444	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133559
dc.description.abstract	© Springer Nature Switzerland AG 2018. One of the latest and most important research topics in the field of information diffusion, which has attracted many social network analyst experts in recent years, is how information is disseminated on social networks. In this paper, a new method is proposed by integration of ant colony algorithm and node centrality to increase the prediction accuracy of information diffusion paths on social networks. In the first stage of our approach, centrality of all nodes in the network is calculated. Then, based on the distances of nodes in the network and also ant colony algorithm, the optimal path of propagation is detected. After implementation of the proposed method, 4 real social network data sets were used to evaluate its performance. The evaluation results of all methods showed a better outcome for our method.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-030-05345-1_33	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Integrating ant colony algorithm and node centrality to improve prediction of information diffusion in social networks	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11342 LNCS	en_US
utslib.for	0805 Distributed Computing	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	11342 LNCS	en_US

Abstract:

© Springer Nature Switzerland AG 2018. One of the latest and most important research topics in the field of information diffusion, which has attracted many social network analyst experts in recent years, is how information is disseminated on social networks. In this paper, a new method is proposed by integration of ant colony algorithm and node centrality to increase the prediction accuracy of information diffusion paths on social networks. In the first stage of our approach, centrality of all nodes in the network is calculated. Then, based on the distances of nodes in the network and also ant colony algorithm, the optimal path of propagation is detected. After implementation of the proposed method, 4 real social network data sets were used to evaluate its performance. The evaluation results of all methods showed a better outcome for our method.

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http://hdl.handle.net/10453/133559