Improving Link Prediction in Social Network with Population-Based Metaheuristics Algorithm

Link prediction is a new interdisciplinary research direction in social network analysis (SNA) which, existing links are analyzed and future links are predicted among millions of users of social network. There are various prediction models including k-nearest neighbor (kNN), fuzzy inference, SVMs, Bayesian model, Markov model and others. In this paper we use Bayesian model to predict future links in flickr social network dataset, it was includes more than 35,000 users. then we use population-based metaheuristics algorithms to enhance accuracy of Bayesian Network Classifiers in feature Selection. We use two standard metric such as AUC and MAP measures for quantifying the accuracy of prediction algorithms.

Improving Link Prediction in Social Network with Population-Based Metaheuristics Algorithm Keywords:

Link prediction , Bayesian Network , Feature Selection , Social network