Forecasting the Missing Links in Heterogeneous Social Networks

Abstract

Social network analysis has gained attention from several researchers in the past time because of its wide application in capturing social interactions. One of the aims of social network analysis is to recover missing links between the users which may exist in the future but have not yet appeared due to incomplete data. The prediction of hidden or missing links in criminal networks is also a significant problem. The collection of criminal data from these networks appears to be incomplete and inconsistent which is reflected in the structure in the form of missing nodes and links. Many machine learning algorithms are applied for this detection using supervised techniques. But, supervised machine learning algorithms require large datasets for training the link prediction model for achieving optimum results. In this research, we have used a Facebook dataset to solve the problem of link prediction in a network. The two machine learning classifiers applied are LogisticRegression and K-Nearest Neighbour where KNN has higher accuracy than LR. In this article, we have proposed an algorithm Graph Sample Aggregator with Low Reciprocity, (GraphSALR), for the generation of node embeddings in larger graphs which use node feature information