Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/223422
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dc.contributor.authorBiao Wangen_US
dc.contributor.authorGe Chenen_US
dc.contributor.authorLuoyi Fuen_US
dc.contributor.authorLi Songen_US
dc.contributor.authorXinbing Wangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:22:24Z-
dc.date.available2020-04-06T08:22:24Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TKDE.2017.2728064en_US
dc.identifier.urihttp://localhost/handle/Hannan/223422-
dc.description.abstractWith the soaring development of large scale online social networks, online information sharing is becoming ubiquitous everyday. Various information is propagating through online social networks including both the positive and negative. In this paper, we focus on the negative information problems such as the online rumors. Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on a realistic scenario. In addition, different from existing problems of influence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method.en_US
dc.format.extent2168,en_US
dc.format.extent2181en_US
dc.publisherIEEEen_US
dc.relation.haspart7982738.pdfen_US
dc.titleDRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networksen_US
dc.typeArticleen_US
dc.journal.volume29en_US
dc.journal.issue10en_US
Appears in Collections:2017

Files in This Item:
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7982738.pdf1.85 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBiao Wangen_US
dc.contributor.authorGe Chenen_US
dc.contributor.authorLuoyi Fuen_US
dc.contributor.authorLi Songen_US
dc.contributor.authorXinbing Wangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:22:24Z-
dc.date.available2020-04-06T08:22:24Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TKDE.2017.2728064en_US
dc.identifier.urihttp://localhost/handle/Hannan/223422-
dc.description.abstractWith the soaring development of large scale online social networks, online information sharing is becoming ubiquitous everyday. Various information is propagating through online social networks including both the positive and negative. In this paper, we focus on the negative information problems such as the online rumors. Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on a realistic scenario. In addition, different from existing problems of influence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method.en_US
dc.format.extent2168,en_US
dc.format.extent2181en_US
dc.publisherIEEEen_US
dc.relation.haspart7982738.pdfen_US
dc.titleDRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networksen_US
dc.typeArticleen_US
dc.journal.volume29en_US
dc.journal.issue10en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7982738.pdf1.85 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBiao Wangen_US
dc.contributor.authorGe Chenen_US
dc.contributor.authorLuoyi Fuen_US
dc.contributor.authorLi Songen_US
dc.contributor.authorXinbing Wangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:22:24Z-
dc.date.available2020-04-06T08:22:24Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TKDE.2017.2728064en_US
dc.identifier.urihttp://localhost/handle/Hannan/223422-
dc.description.abstractWith the soaring development of large scale online social networks, online information sharing is becoming ubiquitous everyday. Various information is propagating through online social networks including both the positive and negative. In this paper, we focus on the negative information problems such as the online rumors. Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on a realistic scenario. In addition, different from existing problems of influence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method.en_US
dc.format.extent2168,en_US
dc.format.extent2181en_US
dc.publisherIEEEen_US
dc.relation.haspart7982738.pdfen_US
dc.titleDRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networksen_US
dc.typeArticleen_US
dc.journal.volume29en_US
dc.journal.issue10en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7982738.pdf1.85 MBAdobe PDF