Implementation of Connected Dominating Set in Social Networks Using Mention Anomaly

Abstract

Social network sites (SNS) are open platform that allows users to create an account in public sites. Social network site contains more active users, who share the posts, messages, or valuable information in real time. Even though social network provides timely updating still it is hard, because the current event mentions are overwhelmed by other mentions, which are totally irrelevant with the current affairs. The proposed methods mainly concentrate on text mentions in social networks and avoid the current affairs of the social aspect. Here two main methodologies are proposed that will improve the web content mining in social network sites. GBMAD (Geographically based mention anomaly detection), is a one of the best methods to manage dynamic links (i.e., Mentions: shares, likes, re-tweets, comments, etc.). GBMAD detects current events over the particular geographical location and it determines the importance of mentions over the crowd. The main advantage is it dynamically works in the real-time data rather than defined data. The next method is connected dominating set is used for identifying the closely related structure by using graph theory techniques. In the proposed work, the anomaly users are identified using Mention anomaly detection. In the future work, the implementation of Sybil attacks in the network will be identified and prevent from unsolicited friend requests in social networks using graph-based theory.