Recurrent Convolution based Graph Neural Network for Node Classification in Graph structure data

Many datasets in various machine learning applications are structural and naturally represented as graphs. They comprise data from the analyses of social and communication networks, predictions of traffic, and fraud detection. Graphbased Deep Learning (DL) aims to construct and train graph datasets attuned models for various graph-structured based tasks. In this work, we presented a model of Graph Neural Network (GNN) for the node classification task. We have compared our proposed model with a baseline model on three citation network datasets: CORA, PUBMED, and CITESEER. We examined the baseline and proposed models predictions on new data instances by randomly generating binary work vectors concerning the work presence probabilities for all three datasets. The proposed model is significantly better than the baseline model on the CORA and CITESEER datasets.