A comprehensive survey of edge prediction in social networks: Techniques, parameters and challenges. (15th June 2019) - Pandey, Babita; Bhanodia, Praveen Kumar; Khamparia, Aditya; Pandey, Devendra Kumar
Highlights: Type of social network and formulation of the link prediction problem from their context. Overview of most popular link prediction parameters and techniques. Overview of performance matrixes used to evaluate the performance of link prediction techniques. Challenges faced by link prediction techniques in various types of social networks. Abstract: Recent development in the area of social networks has sought attention of the researchers to crunch and analyse the data and information of the users to retrieve relevant knowledge for further predictions and recommendations. Edge prediction is one such instance of social network analysis problem exploiting the prevailing data and information pertaining to the network such as: the attributes of the nodes and edges connecting the nodes in order to predict relationships potentially likely to exist in near future. Edge prediction has various applications in significant areas such as: knowledge mining, business recommendation systems, expert systems and bio informatics. In this work, we have classified the edge prediction problem in social network from five aspects: type of SN, feature used for edge prediction, edge prediction method, solution to edge prediction problem and performance measure. The strength of this article is the categorical review of the edge prediction methods in way to draw specific research problems to address further such as: complexity, accuracy, computational overhead and cost, scalability, generalization and performance issues. In addition to this, we have also provided an insightful of edge prediction method applied across various social network categories to understand the advantages and disadvantages to derive future work. The experimental exercise on real world social network particularly Face-book exhibits that the computation time taken in processing large network could be improved significantly may be through distributed techniques or so as the performance of edge prediction methods degrades with the scalability of the social networks. We did not focused upon any appropriate edge prediction methodology as it is out of the scope of the paper because we have exclusively reviewed the existing work done and we are exploring an appropriate ensemble method to precisely predict the future edges between nodes.