In this paper, we study a novel meta aggregation scheme towards binarizing
graph neural networks (GNNs). We begin by developing a vanilla 1-bit GNN
framework that binarizes both the GNN parameters and the graph features.
Despite the lightweight architecture, we observed that this vanilla framework
suffered from insufficient discriminative power in distinguishing graph
topologies, leading to a dramatic drop in performance. This discovery motivates
us to devise meta aggregators to improve the expressive power of vanilla
binarized GNNs, of which the aggregation schemes can be adaptively changed in a
learnable manner based on the binarized features. Towards this end, we propose
two dedicated forms of meta neighborhood aggregators, an exclusive meta
aggregator termed as Greedy Gumbel Neighborhood Aggregator (GNA), and a
diffused meta aggregator termed as Adaptable Hybrid Neighborhood Aggregator
(ANA). GNA learns to exclusively pick one single optimal aggregator from a pool
of candidates, while ANA learns a hybrid aggregation behavior to simultaneously
retain the benefits of several individual aggregators. Furthermore, the
proposed meta aggregators may readily serve as a generic plugin module into
existing full-precision GNNs. Experiments across various domains demonstrate
that the proposed method yields results superior to the state of the art.

该论文主要研究了一种新颖的元聚合方案，以二值化图神经网络（GNNs）为目标，并提出了两种元邻域聚合方法来提高 GNNs 的表达能力，实验表明，该方法的表现优于现有技术。