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ITERATIVE DEEP GRAPH LEARNING FOR GRAPH NEURAL NETWORKS
ITERATIVE DEEP GRAPH LEARNING FOR GRAPH NEURAL NETWORKS
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机译:图形神经网络学习的迭代深图学习
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摘要
An initial noisy graph topology is obtained and an initial adjacency matrix is generated by a similarity learning component using similarity learning and a similarity metric function. An updated adjacency matrix with node embeddings is produced from the initial adjacency matrix using a graph neural network (GNN). The node embeddings are fed back to revise the similarity learning component. The generating, producing, and feeding back operations are repeated for a plurality of iterations.
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