Similarity Index based Link Prediction Algorithms in Social Networks: A Survey, Journal of Telecommunications and Information Technology, 2016, nr 2

Social networking sites have gained much popularity in the recent years. With millions of people connected virtually generate loads of data to be analyzed to infer meaningful associations among links. Link prediction algorithm is one such problem, wherein existing nodes, links and their attributes are analyzed to predict the possibility of potential links, which are likely to happen over a period of time. In this survey, the local structure based link prediction algorithms existing in literature with their features and also the possibility of future research directions is reported and discussed. This survey serves as a starting point for beginners interested in understanding link prediction or similarity index algorithms in general and local structure based link prediction algorithms in particular

Topological and Attribute Link Prediction using Firefly algorithm

Link prediction problem has received remarkable interest in recent past. In this paper, firefly swarm intelligence algorithm is used to perform link prediction exploiting the topological and node attribute features of social network. Fireflies will be made to traverse on nodes and edges of social networks and the brightness of fireflies will play a major role in their movement. Common neighbor method of link prediction is used to compute similarity score upon each iteration. Performance of the proposed algorithm were analyzed over standard data sets using validation method called ten-fold method. The accuracy of proposed work is measured in terms of Area Under the Curve Characteristics (AUC), Recall and Precision. Experimental results showed that the proposed work outperforms the methods proposed in the literature