文章基本信息

标题：MALLET-Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph
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作者：Janani K ; Narmatha S
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：2
DOI：10.15680/ijircce.2015.0302025
出版社：S&S Publications
摘要：Online Social Media Networks (OSNs) provide an online service for building social relations amongusers to share interests, images, audios and videos. A social network service represents each user’s social links such aslikes, comments, favorites and tags which are very useful for mining social influence. The social links indicate certaininfluence in the community. The existing system suffers from analyzing the generic influence but ignoring the moreimportant topic-level influence. Since the content of interest is essentially topic-specific, the underlying social influenceis topic-sensitive. To address these restrictions develop a Novel Topic-Sensitive Influencer Mining (TSIM) frameworkin social networks which aims to mine topic-specific influential nodes in the networks and find topical influential usersand images. The influence estimation is achieved by using hyper graph learning approach in which the verticesrepresent users and images, and the edges represent multi-type relations include visual-textual content relations amongimages, and social link relations between users and images. Social influence mining is used in real applications likefriend suggestion, photo recommendation, expert identification and social search. The proposed algorithm providesprivacy framework for each user in Social Networks like Flickr.
关键词：Hypergraph learning; Influencer mining; Multiparty Access Control; Topic modeling; Topic influence;Topic distribution learning.