In Collective Behavior we come to know how individuals behave in social network environment. The main aim is to guess how there is collective behavior in social media. Many social media face this problem of prediction. Due to the non homogenous nature of connections present in the network a social-dimensions based approach is shown. There are many numbers of actors present in social media. So because of this problem a new technique of edge centric clustering is studied over here which extracts sparse social dimensions. The sparse social network efficiently handles a huge network of actors which gives better performance to all other non sacalable methods.

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Social Dimensions Community Detection Behavior Prediction Classification With Network Data