Graph neural network method based on graph structure enhancement
In response to the problem of sudden performance degradation in graph convolutional networks(GCNs)facing low homogeneity graph structures,a novel graph structure enhancement method is proposed for learning im-proved graph node representations.Firstly,the node information is propagated and aggregated by messages to obtain an initial representation of the nodes.Then the similarity metric of the node representation is calculated to obtain the homogeneous structure of the graph.Finally,the original structure of the graph and the homogeneous structure are fused for node information transfer to obtain the node representation for downstream tasks.The re-sults show that the proposed algorithm outperforms the comparison algorithm in several metrics of node classifica-tion on six publicly available datasets,especially on the four datasets with low homogeneity,the ACC scores of the proposed algorithm exceed the highest benchmark by 5.53％,6.87％,3.08％and 4.00％,and the Fl values exceed the highest benchmark by 5.75％,8.06％,6.46％and 5.61％,respectively,obtaining superior perfor-mance well above the benchmark,indicating that the proposed method successfully improves the structure of graph data and verifies the effectiveness of the algorithm for graph structure optimization.
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