A Link Prediction Approach for Drug Recommendation in Disease-Drug Bipartite Network

Abstract

Social networks we have encountered in different areas and in different forms have a dynamic structure because the relationships they define constantly change. Link prediction is an important and effective solution to understand this dynamic nature of networks and to identify future relations. It estimates of possible future connections between nodes in the network taking advantage of network's current state. In this study, a method for link prediction in the disease-drug network is proposed. Sofar, the most of studies done is usually based on connection prediction in single mode networks. This method has been applied on a bipartite such as disease-drug network, as apart from single mode networks. To compare performance of the proposed method, four of similarity based link prediction methods has been also applied to the network. The results obtained from experiments show that the proposed method has a good percentage of success than the other similarity based link prediction methods.