Author Description

Graph Neural Networks (GNNs) have been successfully applied in many areas to solve real-world problems. Among various architectures of GNNs, the class of spatial-based convolutional GNNs (Conv-GNNs) has gained particular attention due to its simplicity yet effectiveness. The essence of a spatial-based Conv-GNN is a message passing scheme which aggregates neighborhood information to update a focal node's representation. Many of these models are referred to as shallow models due to the over-smoothing issue such that their performance degrades significantly when models go deep. Such shallow models have limited capability to capture information from high-order neighborhood and therefore may suffer from information loss. In this dissertation, I propose Graph Tree Networks (GTNets), a deep graph learning architecture with a new general message passing scheme which follows the nature of message passing in the tree representation of a graph where 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. In GTNet, a focal node's representation is updated by aggregating its initial feature and its neighbor nodes' updated hidden features. Different aggregators in GTNet lead to various graph tree network models.