Graph Tree Neural Networks
  Graph neural networks (GNNs) have recently shown good performance in various
fields. In this paper, we propose graph tree neural networks (GTNNs) designed
to solve the problems of existing networks by analyzing the structure of human
neural networks. In GTNNs, information units are related to the form of a graph
and then they become a bigger unit of information again and have a relationship
with other information units. At this point, the unit of information is a set
of neurons, and we can express it as a vector with GTNN. Defining the starting
and ending points in a single graph is difficult, and a tree cannot express the
relationship among sibling nodes. However, a graph tree can be expressed using
leaf and root nodes as its starting and ending points and the relationship
among sibling nodes. Depth-first convolution (DFC) encodes the interaction
result from leaf nodes to the root node in a bottom-up approach, and
depth-first deconvolution (DFD) decodes the interaction result from the root
node to the leaf nodes in a top-down approach. GTNN is data-driven learning in
which the number of convolutions varies according to the depth of the tree.
Moreover, learning features of different types together is possible.
Supervised, unsupervised, and semi-supervised learning using graph tree
recursive neural network (GTR) , graph tree recursive attention networks
(GTRAs), and graph tree recursive autoencoders (GTRAEs) are introduced in this
paper. We experimented with a simple toy test with source code dataset.
