Graph neural architecture search (NAS) has gained popularity in automatically
designing powerful graph neural networks (GNNs) with relieving human efforts.
However, existing graph NAS methods mainly work under the homophily assumption
and overlook another important graph property, i.e., heterophily, which exists
widely in various real-world applications. To date, automated heterophilic
graph learning with NAS is still a research blank to be filled in. Due to the
complexity and variety of heterophilic graphs, the critical challenge of
heterophilic graph NAS mainly lies in developing the heterophily-specific
search space and strategy. Therefore, in this paper, we propose a novel
automated graph neural network on heterophilic graphs, namely Auto-HeG, to
automatically build heterophilic GNN models with expressive learning abilities.
Specifically, Auto-HeG incorporates heterophily into all stages of automatic
heterophilic graph learning, including search space design, supernet training,
and architecture selection. Through the diverse message-passing scheme with
joint micro-level and macro-level designs, we first build a comprehensive
heterophilic GNN search space, enabling Auto-HeG to integrate complex and
various heterophily of graphs. With a progressive supernet training strategy,
we dynamically shrink the initial search space according to layer-wise
variation of heterophily, resulting in a compact and efficient supernet. Taking
a heterophily-aware distance criterion as the guidance, we conduct heterophilic
architecture selection in the leave-one-out pattern, so that specialized and
expressive heterophilic GNN architectures can be derived. Extensive experiments
illustrate the superiority of Auto-HeG in developing excellent heterophilic
GNNs to human-designed models and graph NAS models.

本文提出了一种自动在异构图上构建图神经网络模型的方法 Auto-HeG，其将异构性融入了异构图神经网络的所有阶段，包括搜索空间设计、超网训练和架构选择，并在离一不作模式下进行了异构架构选择。研究结果表明，Auto-HeG 在开发出优秀的异构 GNN 方面优于人类设计模型和图 NAS 模型。

Auto-HeG: 异构图上的自动化图神经网络

Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs

Recently, Graph Neural Networks (GNNs) have gained popularity in a variety of
real-world scenarios. Despite the great success, the architecture design of
GNNs heavily relies on manual labor. Thus, automated graph neural network
(AutoGNN) has attracted interest and attention from the research community,
which makes significant performance improvements in recent years. However,
existing AutoGNN works mainly adopt an implicit way to model and leverage the
link information in the graphs, which is not well regularized to the link
prediction task on graphs, and limits the performance of AutoGNN for other
graph tasks. In this paper, we present a novel AutoGNN work that explicitly
models the link information, abbreviated to AutoGEL. In such a way, AutoGEL can
handle the link prediction task and improve the performance of AutoGNNs on the
node classification and graph classification task. Specifically, AutoGEL
proposes a novel search space containing various design dimensions at both
intra-layer and inter-layer designs and adopts a more robust differentiable
search algorithm to further improve efficiency and effectiveness. Experimental
results on benchmark data sets demonstrate the superiority of AutoGEL on
several tasks.

本文介绍了一种新型自动图神经网络工作 AutoGEL，该网络在显式建模链接信息的基础上，采用不同 iable Search Algorithm 并提出了一种包含各种设计维度的搜索空间，有效地提高了 AutoGNN 的性能，进而改进了结点分类和图分类任务。