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Topic-Sensitive Influencer Mining in Interest-Based Social Media Networks via Hypergraph Learning
Fang, Quan1; Sang, Jitao1; Xu, Changsheng1; Rui, Yong2
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
2014-04-01
Volume16Issue:3Pages:796-812
SubtypeArticle
AbstractSocial media is emerging as a new mainstream means of interacting around online media. Social influence mining in social networks is therefore of critical importance in real-world applications such as friend suggestion and photo recommendation. Social media is inherently multimodal, including rich types of user contributed content and social link information. Most of the existing research suffers from two limitations: 1) only utilizing the textual information, and/or 2) only analyzing the generic influence but ignoring the more important topic-level influence. To address these limitations, in this paper we develop a novel Topic-Sensitive Influencer Mining (TSIM) framework in interest-based social media networks. Specifically, we take Flickr as the study platform. People in Flickr interact with each other through images. TSIM aims to find topical influential users and images. The influence estimation is determined with a hypergraph learning approach. In the hypergraph, the vertices represent users and images, and the hyperedges are utilized to capture multi-type relations including visual-textual content relations among images, and social links between users and images. Algorithmwise, TSIM first learns the topic distribution by leveraging user-contributed images, and then infers the influence strength under different topics for each node in the hypergraph. Extensive experiments on a real-world dataset of more than 50 K images and 70 K comment/favorite links from Flickr have demonstrated the effectiveness of our proposed framework. In addition, we also report promising results of friend suggestion and photo recommendation via TSIM on the same dataset.
KeywordHypergraph Learning Influencer Mining Topic Modeling
WOS HeadingsScience & Technology ; Technology
WOS KeywordNONLINEAR DIMENSIONALITY REDUCTION ; IMAGE RETRIEVAL ; CLASSIFICATION
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000333111500019
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/2849
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Microsoft Res Asia, Beijing 100080, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Fang, Quan,Sang, Jitao,Xu, Changsheng,et al. Topic-Sensitive Influencer Mining in Interest-Based Social Media Networks via Hypergraph Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2014,16(3):796-812.
APA Fang, Quan,Sang, Jitao,Xu, Changsheng,&Rui, Yong.(2014).Topic-Sensitive Influencer Mining in Interest-Based Social Media Networks via Hypergraph Learning.IEEE TRANSACTIONS ON MULTIMEDIA,16(3),796-812.
MLA Fang, Quan,et al."Topic-Sensitive Influencer Mining in Interest-Based Social Media Networks via Hypergraph Learning".IEEE TRANSACTIONS ON MULTIMEDIA 16.3(2014):796-812.
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