Survey on Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph

A social networking service is an online service, platform, or site that focuses on facilitating the building of social networks or social relations among people who, for example, share interests, activities, backgrounds, or reallife connections. A social network service consists of a representation of each user, his/her social links, and a variety of additional services. Most social network services are web-based and provide means for users to interact over the Internet, such as e-mail and instant messaging. Online community services are sometimes considered as a social network service, though in a broader sense, social network service usually means an individual-centered service whereas online community services are group-centered. Social networking sites allow users to share ideas, activities, events, and interests within their individual networks. The large-scale user-contributed content contains rich social media information such as tags, views, favorites, and comments, which are very useful for mining social influence. The social links such as views, favorites, and re tweets, indicate certain influence in the community. Since the content of interest is essentially topic-specific, the underlying social influence is topic-sensitive. Novel Topic-Sensitive Influencer Mining (TSIM) framework aims to mine topic-specific influential nodes in the networks and find topical influential users and images. The influence estimation is determined with a hyper graph learning approach. In the hyper graph, the vertices represent users and images, and the hyper edges are utilized to capture multitype relations including visual-textual content relations among images, and social links between users and images. The influence estimation is determined with a hyper graph learning approach. In the hyper graph, the vertices represent users and images, and the hyper edges are utilized to capture multitype relations including visual-textual content relations among images, and social links between users and images. Keywords—Hypergraph learning, Influencer mining, Topic modeling, Topic influence, Topic distribution learning.