Graph neural networks learn node embeddings by recursively sampling and aggregating information from nodes in a graph.However,the relatively fixed pattern of existing methods in node sampling and aggregation results in inadequate capture of local pattern diversity,thereby degrading the performance of the model.To solve this problem,a node-level adaptive graph convolutional neural network(NA-GCN)is proposed.A sampling strategy based on node importance is designed to adaptively determine the neighborhood size of each node.An aggregation strategy based on the self-attention mechanism is presented to adaptively fuse the node information within a given neighborhood.Experimental results on multiple benchmark graph datasets show the superiority of NA-GCN in node classification tasks.
关键词
自适应采样/自适应聚合/节点分类/图神经网络(GNNs)/谱图理论
Key words
Adaptive Sampling/Adaptive Aggregation/Node Classification/Graph Neural Networks(GNNs)/Spectral Graph Theory
维普
万方数据