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Conference Paper: Online influence mximization in non-stationary social networks

TitleOnline influence mximization in non-stationary social networks
Authors
Issue Date2016
PublisherIEEE Communications Society, Association for Computing Machinery (ACM).
Citation
The 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS), Beijing, China, 19-22 June 2016. In Conference Proceedings, 2016, p. 1-6 How to Cite?
AbstractSocial networks have been popular platforms for information propagation. An important use case is viral marketing: given a promotion budget, an advertiser can choose some influential users as the seed set and provide them free or discounted sample products; in this way, the advertiser hopes to increase the popularity of the product in the users' friend circles by the world-of-mouth effect, and thus maximizes the number of users that information of the production can reach. There has been a body of literature studying the influence maximization problem. Nevertheless, the existing studies mostly investigate the problem on a one-off basis, assuming fixed known influence probabilities among users, or the knowledge of the exact social network topology. In practice, the social network topology and the influence probabilities are typically unknown to the advertiser, which can be varying over time, i.e., in cases of newly established, strengthened or weakened social ties. In this paper, we focus on a dynamic non-stationary social network and design a randomized algorithm, RSB, based on multi-armed bandit optimization, to maximize influence propagation over time. The algorithm produces a sequence of online decisions and calibrates its explore-exploit strategy utilizing outcomes of previous decisions. It is rigorously proven to achieve an upper-bounded regret in reward and applicable to large-scale social networks. Practical effectiveness of the algorithm is evaluated using both synthetic and real-world datasets, which demonstrates that our algorithm outperforms previous stationary methods under non-stationary conditions.
Persistent Identifierhttp://hdl.handle.net/10722/230546

 

DC FieldValueLanguage
dc.contributor.authorBao, Y-
dc.contributor.authorWang, X-
dc.contributor.authorWang, Z-
dc.contributor.authorWu, C-
dc.contributor.authorLau, FCM-
dc.date.accessioned2016-08-23T14:17:40Z-
dc.date.available2016-08-23T14:17:40Z-
dc.date.issued2016-
dc.identifier.citationThe 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS), Beijing, China, 19-22 June 2016. In Conference Proceedings, 2016, p. 1-6-
dc.identifier.urihttp://hdl.handle.net/10722/230546-
dc.description.abstractSocial networks have been popular platforms for information propagation. An important use case is viral marketing: given a promotion budget, an advertiser can choose some influential users as the seed set and provide them free or discounted sample products; in this way, the advertiser hopes to increase the popularity of the product in the users' friend circles by the world-of-mouth effect, and thus maximizes the number of users that information of the production can reach. There has been a body of literature studying the influence maximization problem. Nevertheless, the existing studies mostly investigate the problem on a one-off basis, assuming fixed known influence probabilities among users, or the knowledge of the exact social network topology. In practice, the social network topology and the influence probabilities are typically unknown to the advertiser, which can be varying over time, i.e., in cases of newly established, strengthened or weakened social ties. In this paper, we focus on a dynamic non-stationary social network and design a randomized algorithm, RSB, based on multi-armed bandit optimization, to maximize influence propagation over time. The algorithm produces a sequence of online decisions and calibrates its explore-exploit strategy utilizing outcomes of previous decisions. It is rigorously proven to achieve an upper-bounded regret in reward and applicable to large-scale social networks. Practical effectiveness of the algorithm is evaluated using both synthetic and real-world datasets, which demonstrates that our algorithm outperforms previous stationary methods under non-stationary conditions.-
dc.languageeng-
dc.publisherIEEE Communications Society, Association for Computing Machinery (ACM).-
dc.relation.ispartofACM/IEEE International Symposium on Quality of Service, ACM/IEEE IWQoS 2016-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleOnline influence mximization in non-stationary social networks-
dc.typeConference_Paper-
dc.identifier.emailWu, C: cwu@cs.hku.hk-
dc.identifier.emailLau, FCM: fcmlau@cs.hku.hk-
dc.identifier.authorityWu, C=rp01397-
dc.identifier.authorityLau, FCM=rp00221-
dc.description.naturepostprint-
dc.identifier.hkuros261736-
dc.identifier.spage1-
dc.identifier.epage6-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160908-

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