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Graph convolutional neural networks have received a lot of attention in various tasks dealing with graph data by aggregating information from neighboring nodes and passing node information. Many recent studies have looked at the impact of topological features on node classification tasks by altering aggregation based on degree values or incorporating topological data analysis into graph convolutional neural networks; however, graph data itself has many topological characteristics in complex networks. In most circumstances, the graph&#x2019;s topological characteristics reveal the nodes&#x2019; similarity and facilitate the node classification task. This paper proposes a topological structure feature extraction method based on the concept of complex topological characteristics, which can obtain deeper topological features in the graph structure and use node features to obtain feature information that is more important for classification from both feature spaces. Evidence from experimental studies has established that the topological structure obtained by the method in this paper can be used as input to the GCN,and good results can be achieved on the classification task even without any external information on the nodes. In the graph dataset of connected topologies, the method exhibits a very large increase in accuracy and macro F1-score when compared to the state-of-the-art baseline model after mixing the node features.

Topological Graph Convolutional Network Based on Complex Network Characteristics