Learning collective behavior of social media using minimum spanning tree algorithm

In this paper we have implemented minimum spanning tree (MST) to predict collective behavior. Collective behavior means to understand how the individual behaves in social networking environment. There is problem of prediction on sites as there are many numbers of actors. We have used a minimum spanning tree algorithm to extract sparse social dimensions. Using this technique we could find the shortest path within nodes and on the basis of that link prediction is done. The experimental results show that minimum spanning tree gives better results as compared to the other algorithms.