Topic-Sensitive Influencer Mining in Interest-Based Social Media Networks via Hypergraph Learning

	Topic-Sensitive Influencer Mining in Interest-Based Social Media Networks via Hypergraph Learning
	Fang, Quan1 ; Sang, Jitao1 ; Xu, Changsheng1 ; Rui, Yong 2
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
	2014-04-01
卷号	16 期号:3 页码:796-812
文章类型	Article
摘要	Social 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.
关键词	Hypergraph Learning Influencer Mining Topic Modeling
WOS标题词	Science & Technology ; Technology
关键词[WOS]	NONLINEAR DIMENSIONALITY REDUCTION ; IMAGE RETRIEVAL ; CLASSIFICATION
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000333111500019
引用统计	被引频次：70[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/2849
专题	多模态人工智能系统全国重点实验室_多媒体计算
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Microsoft Res Asia, Beijing 100080, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 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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