Link prediction and classification in social networks and its application in healthcare

Prediction is one of the most attractive aspects in data mining. Link prediction has recently attracted the attention of many researchers as an effective technique to be used in social network analysis to understand the associations between nodes in social communities. It has been shown in the literature that the link prediction technique is limited to predict the existence of the links in the future. To the best of our knowledge, none of the previous works in this area has explored the prediction of the links that could disappear in the future. In this paper, we propose a link prediction model that is capable of predicting link that might exist and links that may disappear in the future. The model has been successfully applied in two different domains, namely health care and stock market. We have tested our model using different classifiers and the reported results are encouraging.