We propose training-free graph neural networks (TFGNNs), which can be used
without training and can also be improved with optional training, for
transductive node classification. We first advocate labels as features (LaF),
which is an admissible but not explored technique. We show that LaF provably
enhances the expressive power of graph neural networks. We design TFGNNs based
on this analysis. In the experiments, we confirm that TFGNNs outperform
existing GNNs in the training-free setting and converge with much fewer
training iterations than traditional GNNs.

我们提出了无需训练且可选择性训练的图神经网络（TFGNNs），用于跨样本节点分类。我们首先倡导标签作为特征（LaF）的技术，证明了 LaF 能够显著增强图神经网络的表达能力。我们基于这一分析设计了 TFGNNs，并在实验中证实了 TFGNNs 在无需训练的情况下优于现有的 GNNs，并且收敛所需的训练迭代次数少于传统的 GNNs。

无需训练的图神经网络与标签作为特征的能力

Training-free Graph Neural Networks and the Power of Labels as Features

Graph Neural Networks (GNNs) have been widely used to learn node
representations and with outstanding performance on various tasks such as node
classification. However, noise, which inevitably exists in real-world graph
data, would considerably degrade the performance of GNNs revealed by recent
studies. In this work, we propose a novel and robust GNN encoder, Low-Rank
Graph Contrastive Learning (LR-GCL). Our method performs transductive node
classification in two steps. First, a low-rank GCL encoder named LR-GCL is
trained by prototypical contrastive learning with low-rank regularization.
Next, using the features produced by LR-GCL, a linear transductive
classification algorithm is used to classify the unlabeled nodes in the graph.
Our LR-GCL is inspired by the low frequency property of the graph data and its
labels, and it is also theoretically motivated by our sharp generalization
bound for transductive learning. To the best of our knowledge, our theoretical
result is among the first to theoretically demonstrate the advantage of
low-rank learning in graph contrastive learning supported by strong empirical
performance. Extensive experiments on public benchmarks demonstrate the
superior performance of LR-GCL and the robustness of the learned node
representations. The code of LR-GCL is available at
https://anonymous.4open.science/r/Low-Rank_Graph_Contrastive_Learning-64A6/.

我们提出了一种新颖而稳健的图神经网络编码器，低秩图对比学习（LR-GCL），并通过低秩正则化方法进行原型对比学习训练，然后使用 LR-GCL 生成的特征进行线性传导分类算法来对图中的未标记节点进行分类。我们的方法受到图数据和标签的低频属性以及我们对传导学习的尖锐一般化界限的理论动机，是在图对比学习中低秩学习优势在理论上得到证明的第一批理论结果之一。广泛的公共基准实验表明了 LR-GCL 的卓越性能和所学节点表示的鲁棒性。

节点分类的低秩图对比学习

Low-Rank Graph Contrastive Learning for Node Classification

Graph Neural Networks (GNNs) are the predominant technique for learning over
graphs. However, there is relatively little understanding of why GNNs are
successful in practice and whether they are necessary for good performance.
Here, we show that for many standard transductive node classification
benchmarks, we can exceed or match the performance of state-of-the-art GNNs by
combining shallow models that ignore the graph structure with two simple
post-processing steps that exploit correlation in the label structure: (i) an
"error correlation" that spreads residual errors in training data to correct
errors in test data and (ii) a "prediction correlation" that smooths the
predictions on the test data. We call this overall procedure Correct and Smooth
(C&S), and the post-processing steps are implemented via simple modifications
to standard label propagation techniques from early graph-based semi-supervised
learning methods. Our approach exceeds or nearly matches the performance of
state-of-the-art GNNs on a wide variety of benchmarks, with just a small
fraction of the parameters and orders of magnitude faster runtime. For
instance, we exceed the best known GNN performance on the OGB-Products dataset
with 137 times fewer parameters and greater than 100 times less training time.
The performance of our methods highlights how directly incorporating label
information into the learning algorithm (as was done in traditional techniques)
yields easy and substantial performance gains. We can also incorporate our
techniques into big GNN models, providing modest gains. Our code for the OGB
results is at this https URL

通过实验证明，在许多标准的节点分类基准测试中，结合忽略图结构的浅层模型和利用标签结构相关性的两个简单的后处理步骤可以比 GNN 在性能上优秀或者与之匹配，同时仅需使用一小部分 GNN 的参数并拥有更快的运行时间。