Learning How to Propagate Messages in Graph Neural Networks
Teng Xiao Zhengyu Chen Donglin Wang Suhang Wang
This paper studies the problem of learning message propagation strategies for
graph neural networks (GNNs). One of the challenges for graph neural networks
is that of defining the propagation strategy. For instance, the choices of
propagation steps are often specialized to a single graph and are not
personalized to different nodes. To compensate for this, in this paper, we
present learning to propagate, a general learning framework that not only
learns the GNN parameters for prediction but more importantly, can explicitly
learn the interpretable and personalized propagate strategies for different
nodes and various types of graphs. We introduce the optimal propagation steps
as latent variables to help find the maximum-likelihood estimation of the GNN
parameters in a variational Expectation-Maximization (VEM) framework. Extensive
experiments on various types of graph benchmarks demonstrate that our proposed
framework can significantly achieve better performance compared with the
state-of-the-art methods, and can effectively learn personalized and
interpretable propagate strategies of messages in GNNs.