Link Prediction in Social Networks Using Proximity-Based Algorithms

Abstract

There has been an overwhelming increase in social media users in today's world. This ever-increasing data of the Social Network poses a challenge for Link Prediction analysis. The association between users that is not present but has a possibility of existing in the future can be predicted by Link Prediction techniques. In Social Networks, Link Prediction can be employed to monitor social interactions & anomalies, suggest friends to the users and also to analyze the influence or detect communities. Link Prediction helps in retaining the users for longer duration and hence there is a boost in the engagement rate. The more accurate the link prediction is the higher the engagement rate of the applications. Social Networks like Facebook, E-business organisations Zomato and Amazon employ Link Prediction in various forms to boost their revenue and user-experience. There are various algorithms that help in calculation of the possibility of link between entities. The algorithm selection will be based on the specific use case requirement of the applications. The authors of this paper discuss Jaccard Coefficient and Resource Allocation Proximity-based algorithms for Link Prediction. The comparative study is conducted for each of the algorithms and it is observed that the combination of both the algorithms yields a better result than either of them.