  We propose Graph Tree Networks (GTNets), a deep graph learning architecture
with a new general message passing scheme that originates from the tree
representation of graphs. In the tree representation, messages propagate upward
from the leaf nodes to the root node, and each node preserves its initial
information prior to receiving information from its child nodes (neighbors). We
formulate a general propagation rule following the nature of message passing in
the tree to update a node's feature by aggregating its initial feature and its
neighbor nodes' updated features. Two graph representation learning models are
proposed within this GTNet architecture - Graph Tree Attention Network (GTAN)
and Graph Tree Convolution Network (GTCN), with experimentally demonstrated
state-of-the-art performance on several popular benchmark datasets. Unlike the
vanilla Graph Attention Network (GAT) and Graph Convolution Network (GCN) which
have the "over-smoothing" issue, the proposed GTAN and GTCN models can go deep
as demonstrated by comprehensive experiments and rigorous theoretical analysis.

Machine Learning;Artificial Intelligence