Title: New GCNN-Based Architecture for Semi-Supervised Node Classification. Abstract: The nodes of a graph existing in a specific cluster are more likely to connect to each other than with other nodes in the graph. Then revealing some information about the nodes, the structure of the graph (the graph edges) provides this opportunity to know more information about the other nodes. From this perspective, this paper revisits the node classification task in a semi-supervised scenario by graph convolutional neural network. The goal is to benefit from the flow of information that circulates around the revealed node labels. For this aim, this paper provides a new graph convolutional neural network architecture. This architecture benefits efficiently from the revealed training nodes, the node features, and the graph structure. On the other hand, in many applications, non-graph observations (side information) exist beside a given graph realization. The non-graph observations are usually independent of the graph structure. This paper shows that the proposed architecture is also powerful in combining a graph realization and independent non-graph observations. For both cases, the experiments on the synthetic and real-world datasets demonstrate that our proposed architecture achieves a higher prediction accuracy in comparison to the existing state-of-the-art methods for the node classification task.