首页|Graph convolutional network combined with random walks and graph attention network for node classification
Graph convolutional network combined with random walks and graph attention network for node classification
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NETL
NSTL
万方数据
Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor nodes.However,this weighting mechanism is learned based on static information,which means it is susceptible to noisy nodes and edges,resulting in significant limitations.In this paper,a method is proposed to obtain context dynamically based on random walk,which allows the context-based weighting mechanism to better avoid noise interference.Furthermore,the proposed context-based weighting mechanism is combined with the node content-based weighting mechanism of the graph attention(GAT)network to form a model based on a mixed weighting mechanism.The model is named as the context-based and content-based graph convolutional network(CCGCN).CCGCN can better discover important neighbors,eliminate noise edges,and learn node embedding by message passing.Experiments show that CCGCN achieves state-of-the-art performance on node classification tasks in multiple datasets.
NETL
NSTL
万方数据
