Graph representation learning has recently been applied to a broad spectrum
of problems ranging from computer graphics and chemistry to high energy physics
and social media. The popularity of graph neural networks has sparked interest,
both in academia and in industry, in developing methods that scale to very
large graphs such as Facebook or Twitter social networks. In most of these
approaches, the computational cost is alleviated by a sampling strategy
retaining a subset of node neighbors or subgraphs at training time. In this
paper we propose a new, efficient and scalable graph deep learning architecture
which sidesteps the need for graph sampling by using graph convolutional
filters of different size that are amenable to efficient precomputation,
allowing extremely fast training and inference. Our architecture allows using
different local graph operators (e.g. motif-induced adjacency matrices or
Personalized Page Rank diffusion matrix) to best suit the task at hand. We
conduct extensive experimental evaluation on various open benchmarks and show
that our approach is competitive with other state-of-the-art architectures,
while requiring a fraction of the training and inference time. Moreover, we
obtain state-of-the-art results on ogbn-papers100M, the largest public graph
dataset, with over 110 million nodes and 1.5 billion edges.

本文提出了一种新的、高效且可扩展的图深度学习架构，通过使用不同尺寸的图卷积滤波器，绕过了图采样的必要性，从而允许极快速的训练和推理，同时在比对测试中展现了与其他最先进架构相竞争的表现，对于包含超过 1.1 亿个节点和 15 亿条边的公共图数据集 ogbn-papers100M 实现了最先进的结果。

可扩展的 Inception 图神经网络 (SIGN)

SIGN: Scalable Inception Graph Neural Networks

Network data can be conveniently modeled as a graph signal, where data values
are assigned to nodes of a graph that describes the underlying network
topology. Successful learning from network data is built upon methods that
effectively exploit this graph structure. In this work, we leverage graph
signal processing to characterize the representation space of graph neural
networks (GNNs). We discuss the role of graph convolutional filters in GNNs and
show that any architecture built with such filters has the fundamental
properties of permutation equivariance and stability to changes in the
topology. These two properties offer insight about the workings of GNNs and
help explain their scalability and transferability properties which, coupled
with their local and distributed nature, make GNNs powerful tools for learning
in physical networks. We also introduce GNN extensions using edge-varying and
autoregressive moving average graph filters and discuss their properties.
Finally, we study the use of GNNs in recommender systems and learning
decentralized controllers for robot swarms.

本文利用图信号处理来表征图神经网络（GNNs）的表征空间，讨论了图卷积滤波器在 GNNs 中的作用，并展示了这类滤波器所建立的任何架构都具有置换等变性和对网络拓扑的稳定性的基本属性。然后，我们讨论了扩展了边变量和自回归移动平均图滤波器的 GNNs 以及它们的属性，并最终研究了在推荐系统和学习机器人群体的分散式控制器中使用 GNNs 的相关研究。