SURVEY ON LINK PREDICTION IN FACEBOOK AND TWITTER

Abstract

Social media have gained increased usage rapidly for a discrepancy of reasons. Participants were asked to view one of six mock Twitter.com pages that varied both the number of followers and the ratio between followers and follows on the page and report their perceived source worthy of trust. This research examines mediocre factors and speculative relevant contextual variables that affect sensational formation and the willingness to initiate virtual friendship. In this paper [ Network Analysis of Recurring YouTube Spam Campaigns] demonstrated tracking of spam campaigns over time using network motif profiling. Heterogeneous social network services, such as Facebook and Twitter, have emerged as popular, and often effective channels for Web users to capture updates from their friends. In this paper [Learning to blend vitality rankings from heterogeneous social networks], demonstrated the problem of blending vitality denotes all kinds of updates user receives in various social networks. It proposed variety of content, users, and users correlation features for this task. Since vitalities from different social networks are likely to have different sets of features, employed divide-and-conquer strategy in order to fully exploit all available features for vitalities from each social network respectively.