Composite and Mutual Link Prediction using SVM in Social Networks

Link prediction is a key technique in many applications in social networks; where potential links between entities need to be predicted. Typical link prediction techniques deal with either uniform entities, i.e., company to company, applicant to applicant links, or non-mutual relationships, e.g., company to applicant links. However, there is a challenging problem of link prediction among the composite entities and mutual links; such as accurate prediction of matches on company dataset, jobs or workers on employment websites, where the links are mutually determined by both entities that composite entity belong to disjoint groups. The causes of interactions in these domains makes composite and mutual link prediction significantly different from the typical version of the problem. This work addresses these issues by proposing the Support Vector Machine model. By implementing the proposed algorithm it is expected that the accuracy will get increased in the link prediction problem.