Graph Neural Networks (GNNs) have attracted considerable attention and have
emerged as a new promising paradigm to process graph-structured data. GNNs are
usually stacked to multiple layers and the node representations in each layer
are computed through propagating and aggregating the neighboring node features
with respect to the graph. By stacking to multiple layers, GNNs are able to
capture the long-range dependencies among the data on the graph and thus bring
performance improvements. To train a GNN with multiple layers effectively, some
normalization techniques (e.g., node-wise normalization, batch-wise
normalization) are necessary. However, the normalization techniques for GNNs
are highly task-relevant and different application tasks prefer to different
normalization techniques, which is hard to know in advance. To tackle this
deficiency, in this paper, we propose to learn graph normalization by
optimizing a weighted combination of normalization techniques at four different
levels, including node-wise normalization, adjacency-wise normalization,
graph-wise normalization, and batch-wise normalization, in which the
adjacency-wise normalization and the graph-wise normalization are newly
proposed in this paper to take into account the local structure and the global
structure on the graph, respectively. By learning the optimal weights, we are
able to automatically select a single best or a best combination of multiple
normalizations for a specific task. We conduct extensive experiments on
benchmark datasets for different tasks, including node classification, link
prediction, graph classification and graph regression, and confirm that the
learned graph normalization leads to competitive results and that the learned
weights suggest the appropriate normalization techniques for the specific task.
Source code is released here this https URL

本文提出了通过优化四个不同层面的加权组合来学习图归一化的方法，其中包括节点归一化、邻接归一化、图归一化和批归一化。在不同数据集和任务中，学到的权重可以选择最优的归一化技巧，实现竞争性结果。