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Existing graph neural networks may suffer from the "suspended animationproblem" when the model architecture goes deep. Meanwhile, for some graphlearning scenarios, e.g., nodes with text/image attributes or graphs withlong-distance node correlations, deep graph neural networks will be necessaryfor effective graph representation learning. In this paper, we propose a newgraph neural network, namely DIFNET (Graph Diffusive Neural Network), for graphrepresentation learning and node classification. DIFNET utilizes both neuralgates and graph residual learning for node hidden state modeling, and includesan attention mechanism for node neighborhood information diffusion. Extensiveexperiments will be done in this paper to compare DIFNET against severalstate-of-the-art graph neural network models. The experimental results canillustrate both the learning performance advantages and effectiveness ofDIFNET, especially in addressing the "suspended animation problem".

Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification