Topic-Sensitive Influencer Mining in Interest-Based Social Media Networks via Hypergraph Learning
文献类型：期刊论文
| 作者 | Fang, Quan1 ; Sang, Jitao1; Xu, Changsheng1; Rui, Yong2
|
| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
 |
| 出版日期 | 2014-04-01 |
| 卷号 | 16期号:3页码:796-812 |
| 关键词 | Hypergraph learning influencer mining topic modeling |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
| 研究领域[WOS] | Computer Science ; Telecommunications |
| 关键词[WOS] | NONLINEAR DIMENSIONALITY REDUCTION ; IMAGE RETRIEVAL ; CLASSIFICATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000333111500019 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/2849]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。