LPCNN: convolutional neural network for link prediction based on network structured features

Abstract

In a social network (SN), link prediction (LP) is the process of estimating whether a link will exist in the future. In prior LP papers, heuristics score techniques were used. Recent state-of-the-art studies, like Wesfeiler-Lehman neural machine (WLNM) and learning from subgraphs, embeddings, and attributes for link prediction (SEAL), have demonstrated that heuristics scores may increase LP model accuracy by employing deep learning and sub-graphing techniques. WLNM and SEAL, on the other hand, have some limitations and perform poorly in some kinds of SNs. The goal of this research is to present a new framework for enhancing the effectiveness of LP models throughout various types of social networks while overcoming the constraints of earlier techniques. We present the link prediction based convolutional neural network (LPCNN) framework, which uses deep learning techniques to examine common neighbors and predict relations. Adapts the LP task into an image classification issue and classifies the links using a convolutional neural network. On 10 various types of real-work networks, tested the suggested LP model and compared its performance to heuristics and state-of-the-art approaches. Results revealed that our model outperforms the other LP benchmark approaches with an average area under curved (AUC) above 99%.