Link Prediction Algorithms for Social Networks Based on Machine Learning and HARP

Abstract

In Node2Vec, the global structure of the network is neglected and the stochastic gradient descent (SGD) method is easy to fall into local optimum. Based on this algorithm, an improved link prediction algorithm combining machine learning and hierarchical representation learning for network (HARP) is proposed. This method rst uses adaptive learning optimizer Adam instead of SGD to improve Node2Vec, then divides the nodes and edges of the original network graph into a series of smaller layered graphs by merging them according to HARP, and then uses the improved Node2Vec algorithm to extract features continuously, so as to realize network embedding. Finally, a social network link prediction model based on machine learning and HARP is established. A series of social network link prediction experiments are carried out. The results show that the new method has excellent performance and stability.