Survey on Link Anomaly Detection for Textual Stream in Online Social Network

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) Topic: Data Management Date Added: Dec 2014 Format: PDF
Link anomaly detection is one of the most important topics in social network. Many of the social networks such as Facebook, Google+, LinkedIn, or twitter require an effective and efficient framework to identify deviated data. Anomaly detection methods are typically implemented in social stream mode, and thus cannot be easily extended to large-scale problems without sacrificing computation where the user's link is generated dynamically (replies, mentions, and retweets). A new approach model i.e. probability model, this model to the capture normal linking behavior of a social network users, and propose to detect the trending topic from the social networks through the probability model.

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