{"type":"ScholarlyArticle","title":"Opinion Maximization in Social Trust Networks","authors":[{"name":"Pinghua Xu","slug":"pinghua-xu","given":"Pinghua","family":"Xu","affiliation":"School of Computer Science , National Engineering Research Center for Multimedia Software and; Shenzhen Research Institute , Wuhan University"},{"name":"Wenbin Hu","slug":"wenbin-hu","given":"Wenbin","family":"Hu","affiliation":"School of Computer Science , National Engineering Research Center for Multimedia Software and; Shenzhen Research Institute , Wuhan University"},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":"Department of Computing , Macquarie University"},{"name":"Weiwei Liu","slug":"weiwei-liu","given":"Weiwei","family":"Liu","affiliation":"School of Computer Science , National Engineering Research Center for Multimedia Software and"}],"abstract":"Social media sites are now becoming very important platforms for product promotion or marketing campaigns. Therefore, there is broad interest in determining ways to guide a site to react more positively to a product with a limited budget. However, the practical significance of the existing studies on this subject is limited for two reasons. First, most studies have investigated the issue in oversimplified networks in which several important network characteristics are ignored. Second, the opinions of individuals are modeled as bipartite states (e.g., support or not) in numerous studies, however, this setting is too strict for many real scenarios. In this study, we focus on social trust networks (STNs), which have the significant characteristics ignored in the previous studies. We generalized a famed continuous-valued opinion dynamics model for STNs, which is more consistent with real scenarios. We subsequently formalized two novel problems for solving the issue in STNs. In addition, we developed two matrix-based methods for these two problems and experiments on realworld datasets to demonstrate the practical utility of our methods.","doi":"10.24963/ijcai.2020/174","url":"https://ijcai.org/proceedings/2020/174","pdf":"https://ijcai.org/proceedings/2020/0174.pdf","markdown":"https://ijcai.org/proceedings/2020/174.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/174","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":174,"pages":{"first":1251,"last":1257},"keywords":["Data Mining: Mining Graphs, Semi Structured Data, Complex Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24292/file_24292.m3u8","length_sec":278,"provider":"hls"}],"references":[{"position":1,"raw":"[Abebe et al., 2018] Rediet Abebe, Jon Kleinberg, David Parkes, and Charalampos E. 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