Classification of nodes in citation graphs using graph neural networks

Graphs are data that describe complex relationships between different things in the real world, such as the Internet, social network, bibliographic network, and so on. One of the things that many people deal with today is online social networks. The graph display of online social networks such as Twitter, WeChat and Facebook is not possible today with less than billions of nodes, and for this reason, the study of large-scale network data has become a necessity for researchers. Regarding social networks, online users often have limited information; But for social media service providers, user node information such as interest, beliefs, or other characteristics are very important to customize their services for users in many applications such as recommendations and personalized search, making it a challenge for service providers. An effective way to deal with this challenge is to infer missing user information using pervasive network structures in social media. One of the most important inferences in data mining and network analysis is node classification, which aims to infer the missing labels of nodes based on labeled nodes and network structure. In this research, we have performed the task of node classification on the PubMedDiabetes, CiteSeer and Cora citation network datasets using GraphSAGE, GCN and GAT neural networks and we have generally concluded that the GraphSAGE neural network on the network datasets The cited reference works well for the node classification task.