Influence Maximization with Novelty Decay in Social Networks

Hdl Handle:
http://hdl.handle.net/10149/592761
Title:
Influence Maximization with Novelty Decay in Social Networks
Authors:
Feng, S. (Shanshan); Chen, X. (Xuefeng); Cong, G. (Gao); Zeng, Y. (Yifeng); Chee, Y. M. (Yeow Meng); Xiang, Y. (Yanping)
Affiliation:
Teesside University. Digital Futures Institute
Citation:
Feng, S., Chen, X., Cong, G., Zeng, Y., Chee, Y. M., Xiang, Y. (2014) 'Influence Maximization with Novelty Decay in Social Networks' Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence
Publisher:
AAAI Publications
Conference:
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence
Issue Date:
2014
URI:
http://hdl.handle.net/10149/592761
Additional Links:
http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8485
Abstract:
Influence maximization problem is to find a set of seed nodes in a social network such that their influence spread is maximized under certain propagation models. A few algorithms have been proposed for solving this problem. However, they have not considered the impact of novelty decay on influence propagation, i.e., repeated exposures will have diminishing influence on users. In this paper, we consider the problem of influence maximization with novelty decay (IMND). We investigate the effect of novelty decay on influence propagation on real-life datasets and formulate the IMND problem. We further analyze the problem properties and propose an influence estimation technique. We demonstrate the performance of our algorithms on four social networks.
Type:
Meetings and Proceedings
Language:
en
Rights:
AAAI authors are granted back the right to use their own papers for noncommercial uses. Publisher advice [Recieved: 25/01/2016]

Full metadata record

DC FieldValue Language
dc.contributor.authorFeng, S. (Shanshan)en
dc.contributor.authorChen, X. (Xuefeng)en
dc.contributor.authorCong, G. (Gao)en
dc.contributor.authorZeng, Y. (Yifeng)en
dc.contributor.authorChee, Y. M. (Yeow Meng)en
dc.contributor.authorXiang, Y. (Yanping)en
dc.date.accessioned2016-01-04T12:47:52Zen
dc.date.available2016-01-04T12:47:52Zen
dc.date.issued2014en
dc.identifier.urihttp://hdl.handle.net/10149/592761en
dc.description.abstractInfluence maximization problem is to find a set of seed nodes in a social network such that their influence spread is maximized under certain propagation models. A few algorithms have been proposed for solving this problem. However, they have not considered the impact of novelty decay on influence propagation, i.e., repeated exposures will have diminishing influence on users. In this paper, we consider the problem of influence maximization with novelty decay (IMND). We investigate the effect of novelty decay on influence propagation on real-life datasets and formulate the IMND problem. We further analyze the problem properties and propose an influence estimation technique. We demonstrate the performance of our algorithms on four social networks.en
dc.language.isoenen
dc.publisherAAAI Publicationsen
dc.relation.urlhttp://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8485en
dc.rightsAAAI authors are granted back the right to use their own papers for noncommercial uses. Publisher advice [Recieved: 25/01/2016]en
dc.titleInfluence Maximization with Novelty Decay in Social Networksen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentTeesside University. Digital Futures Instituteen
dc.identifier.conferenceProceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligenceen
or.citation.harvardFeng, S., Chen, X., Cong, G., Zeng, Y., Chee, Y. M., Xiang, Y. (2014) 'Influence Maximization with Novelty Decay in Social Networks' Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligenceen
dc.eprint.versionPost-printen
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