"Prediction of Vital Actions in Active Social Networking Sites using Artificial Intelligence"

A social network is usually conceived as a graph in which individuals in the network are represented by the nodes and the nodes are connected to each other by links which depict the relations among the individuals. The term “community” for any group of nodes that are densely connected among themselves and sparsely connected to others. As time evolves, communities in a social network may undergo various changes (split, expand, shrink, stable, merge) known as critical events. Prediction of critical events is an important and difficult issue in the study of social networks. This paper proposes a sliding window analysis, an autoregressive model and survival analysis techniques. The autoregressive model is here to simulate the evolution of the community structure, and the survival analysis techniques allow the prediction of future changes the community may undergo. In our approach Critical events are treated based on a weighting scheme.