Abstract：A topology graph is utilized to portray the relationship between nodes and node features are updated on the basis of the topology graph, when the graph convolutional neural network is applied for solving node classification problems. However, traditional topology graph can only portray definite relationships between nodes, i.e. fixed values of connected edge weights, ignoring the widespread uncertainty in the real world. The uncertainty affects not only the relationship between nodes, but also the final classification performance of the model. To overcome this defect, a graph convolutional neural network algorithm based on rough graphs is proposed. Firstly, the rough edges are constructed via the upper-lower approximation theory and the edge theory of traditional topology graph, and the uncertain relationships between nodes are inscribed in rough edges using maximum-minimum relationship values coming in pairs to construct a rough graph. Then, an end-to-end trainable neural network architecture based on rough graphs is designed, the rough graphs trained with rough weight coefficients are fed into the graph convolutional neural network, and the node features are updated based on the uncertain information. Finally, nodes are classified according to the learned node features. The experiments on real dataset show that the proposed algorithm improves the accuracy of node classification.
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