Abstract: Streaming graphs are widely used in practical applications,and their node and structure characteristics change dynamically with time.Although Graph Neural Network(GNN) is excellent in static graph node classification,it is difficult to apply it directly to streaming graphs,because the continuous evolution of streaming graphs will lead to information lag and omission,it is difficult for models to accurately extract streaming graph features.To solve the above challenges,the Continuously Evolving Streaming Graph Neural Network(CESGNN) is proposed to solve the node classification problem of streaming graph.Firstly,the Continuous Updates Graph Convolutional Network(CU-GCN) incrementally updates parameters to adapt to changes in the node characteristics of the streaming graph to alleviate the information lag problem.Then the Adaptive Deepening Graph Neural Network(AD-GNN) alleviates the information omission problem by decoupling the aggregation and updates operations to dig deep features of the streaming graph.CESGNN organically combines the original features,the shallow features extracted by CU-GCN,and the deep features extracted by AD-GNN to obtain a more accurate and comprehensive representation of streaming graph features.The experimental results show that CESGNN model has good adaptability and stability of streaming graph,and improves the accuracy of node classification of streaming graph.

Key words: Streaming graphs, Graph Neural Networks, Incrementally updating, Decoupling aggregation and update, Feature fusion