Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Depth-first search and linear grajh algorithms
SWAT '71 Proceedings of the 12th Annual Symposium on Switching and Automata Theory (swat 1971)
Mining of Massive Datasets
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Online social network has become prevalent in our modern lifestyle by which one can easily connect and share information with anybody around the world. Facebook, Twitter, Flicker, Sina Weibo, are some exemplars nowadays. As the population of users in social networks grows, the concern of security in using such network escalates too. The social network is formed by people from all walks of life. Since there is little physical interaction available, it is difficult to verify whether social network users are trustworthy or not. In this paper, we propose a method that assists users to infer the degree of trustworthiness in social network. A quantitative indicator, which we call it Trust Index (TI) is assigned to each user, so one can have a ranked list of users, those with the greatest values of TI appear on top and vice versa. This serves as a reference for a user to decide how much s/he would want to trust them in social networks. TI is calculated based on the distance in terms of hop counts that measures how far apart between the user and s/he peer is. The distance is estimated by referring to relation as well as how acquainted the test user is with respect to some verified icons (public figures which have already been verified by the social network administrators) in social networks. Our TI algorithm also could enlist a group of people whose TIs fall below a given threshold, these are the users that need to be cautious about.