Existing graph neural networks may suffer from the “suspended animation problem” when the model architecture goes deep. Meanwhile, for some graph learning scenarios, e.g., nodes with text/image attributes or graphs with long- distance node correlations, deep graph neural networks will be necessary for effective graph representation learning. In this paper, we propose a new graph neural network, namely DIFNET (Graph Diffusive Neural Network), for deep graph representation learning and node classification. DIFNET utilizes both neural gates and graph residual learning for node hidden state modeling, and includes an attention mechanism for node neighborhood information diffusion. Extensive experimental results can illustrate both the learning performance advantages of DIFNET compared with existing methods, especially in addressing the “suspended animation problem”.
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Jiawei Zhang (Florida State University)
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