Structural Mining for Link Prediction Using Various Machine Learning Algorithms

Abstract

Link prediction is an emerging research problem in social network analysis, where future possible links are predicted based on the structural or the content information associated with the network. In this paper, various machine learning (ML) techniques have been utilized for predicting the future possible links based on the features extracted from the topological structure. Moreover, feature sets have been prepared by measuring different similarity metrics between all pair of nodes between which no link exists. For predicting the future possible links various supervised ML algorithms like K-NN, MLP, bagging, SVM, decision tree have been implemented. The feature set for each instance in the dataset has been prepared by measuring the similarity index between the non-existence links. The model has been trained to identify the new links which are likely to appear in the future but currently do not exist in the network. Further, the proposed model is validated through various performance metrics.