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.
The nodes of a graph existing in a cluster are more likely to connect to each other than with other nodes in the graph. Then revealing some information about some nodes, the structure of the graph ( graph edges) provides this opportunity to know more information about other nodes. From this perspective, this paper revisits the node classification task in a semi-supervised scenario by graph convolutional networks (GCNs) . The goal is to benefit from the flow of information that circulates around the revealed node labels. The contribution of this paper is twofold. First , this paper provides a method for extracting side information from a graph realization. Then a new GCN architecture is presented that combines the output of traditional GCN and the extracted side information. Another contribution of this paper is relevant to non-graph observations ( independent side information) that exists beside a graph realization in many applications. Indeed, the extracted side information can be replaced by a sequence of side information that is independent of the graph structure. For both cases, the experiments on synthetic and real-world datasets demonstrate that the proposed model achieves a higher prediction accuracy in comparison to the existing state-of-the-art methods for the node classification task.
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