  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 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
experiments will be done in this paper to compare DIFNET against several
state-of-the-art graph neural network models. The experimental results can
illustrate both the learning performance advantages and effectiveness of
DIFNET, especially in addressing the "suspended animation problem".
