Link Prediction in Paper Citation Network based on Deep Graph Convolutional Neural Network

Abstract

AbstractWith regards to network structured data nowadays, link prediction stands out as a key problem. It has been observed that interactions between the entities act as the basic foundations of many applications in various fields such as chemistry, biology, or social networks. In the previous studies on this subject, most of the approaches used heuristics methods with an evaluation function to find the similarity index between entities, thereby predicting the possibility of links between them. However, heuristics methods are based on assumptions about the existence of associations between entities; thus its shortcoming is that when these assumptions are not accurate, the results of the algorithm would be reduced significantly. Taking those into considerations, in this paper, we propose a method to solve this problem based on deep graph convolutional neural network. Experimental results on paper citation network dataset have shown that particular and this method is promising.KeywordsGraph neural networksDeep graph convolutional neural networkLink predictionPaper citation network