In recent years, Graph Neural Networks (GNNs) have become the de facto tool
for learning node and graph representations. Most GNNs typically consist of a
sequence of neighborhood aggregation (a.k.a., message passing) layers. Within
each of these layers, the representation of each node is updated from an
aggregation and transformation of its neighbours representations at the
previous layer. The upper bound for the expressive power of message passing
GNNs was reached through the use of MLPs as a transformation, due to their
universal approximation capabilities. However, MLPs suffer from well-known
limitations, which recently motivated the introduction of Kolmogorov-Arnold
Networks (KANs). KANs rely on the Kolmogorov-Arnold representation theorem,
rendering them a promising alternative to MLPs. In this work, we compare the
performance of KANs against that of MLPs in graph learning tasks. We perform
extensive experiments on node classification, graph classification and graph
regression datasets. Our preliminary results indicate that while KANs are
on-par with MLPs in classification tasks, they seem to have a clear advantage
in the graph regression tasks.

最近几年，图神经网络 (GNNs) 成为学习节点和图表示的事实上的工具，该论文比较了 KANs 与 MLPs 在图学习任务中的性能，结果表明在分类任务方面两者相当，但在图回归任务上，KANs 具有明显优势。

KAGNNs: Kolmogorov-Arnold 网络遇上图学习

KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning

The key towards learning informative node representations in graphs lies in
how to gain contextual information from the neighbourhood. In this work, we
present a simple-yet-effective self-supervised node representation learning
strategy via directly maximizing the mutual information between the hidden
representations of nodes and their neighbourhood, which can be theoretically
justified by its link to graph smoothing. Following InfoNCE, our framework is
optimized via a surrogate contrastive loss, where the positive selection
underpins the quality and efficiency of representation learning. To this end,
we propose a topology-aware positive sampling strategy, which samples positives
from the neighbourhood by considering the structural dependencies between nodes
and thus enables positive selection upfront. In the extreme case when only one
positive is sampled, we fully avoid expensive neighbourhood aggregation. Our
methods achieve promising performance on various node classification datasets.
It is also worth mentioning by applying our loss function to MLP based node
encoders, our methods can be orders of faster than existing solutions. Our
codes and supplementary materials are available at
this https URL

本文提出一种利用自监督学习策略来学习图中节点信息表示的简单而有效方法，通过直接最大化节点和它们的邻居的隐藏表示之间的互信息来获得上下文信息，并采用拓扑感知的正样本采样策略，并借助正样本选择提高了表示学习的质量和有效性。