Graph NAS has emerged as a promising approach for autonomously designing GNN
architectures by leveraging the correlations between graphs and architectures.
Existing methods fail to generalize under distribution shifts that are
ubiquitous in real-world graph scenarios, mainly because the graph-architecture
correlations they exploit might be spurious and varying across distributions.
We propose to handle the distribution shifts in the graph architecture search
process by discovering and exploiting the causal relationship between graphs
and architectures to search for the optimal architectures that can generalize
under distribution shifts. The problem remains unexplored with following
challenges: how to discover the causal graph-architecture relationship that has
stable predictive abilities across distributions, and how to handle
distribution shifts with the discovered causal graph-architecture relationship
to search the generalized graph architectures. To address these challenges, we
propose Causal-aware Graph Neural Architecture Search (CARNAS), which is able
to capture the causal graph-architecture relationship during the architecture
search process and discover the generalized graph architecture under
distribution shifts. Specifically, we propose Disentangled Causal Subgraph
Identification to capture the causal subgraphs that have stable prediction
abilities across distributions. Then, we propose Graph Embedding Intervention
to intervene on causal subgraphs within the latent space, ensuring that these
subgraphs encapsulate essential features for prediction while excluding
non-causal elements. Additionally, we propose Invariant Architecture
Customization to reinforce the causal invariant nature of the causal subgraphs,
which are utilized to tailor generalized graph architectures. Extensive
experiments demonstrate that CARNAS achieves advanced out-of-distribution
generalization ability.

通过发现和利用图与体系结构之间的因果关系来搜索能够在分布转移下具有泛化能力的最佳体系结构，以解决现有方法在现实图场景中广泛分布转移下无法泛化的问题。

分布偏移下的因果感知图神经架构搜索

Causal-Aware Graph Neural Architecture Search under Distribution Shifts

Recently, graph neural networks have been adopted in a wide variety of
applications ranging from relational representations to modeling irregular data
domains such as point clouds and social graphs. However, the space of graph
neural network architectures remains highly fragmented impeding the development
of optimized implementations similar to what is available for convolutional
neural networks. In this work, we present BiGraphNet, a graph neural network
architecture that generalizes many popular graph neural network models and
enables new efficient operations similar to those supported by ConvNets. By
explicitly separating the input and output nodes, BiGraphNet: (i) generalizes
the graph convolution to support new efficient operations such as coarsened
graph convolutions (similar to strided convolution in convnets), multiple input
graphs convolution and graph expansions (unpooling) which can be used to
implement various graph architectures such as graph autoencoders, and graph
residual nets; and (ii) accelerates and scales the computations and memory
requirements in hierarchical networks by performing computations only at
specified output nodes.

本文通过提出一种名为 BiGraphNet 的图神经网络架构，加速和扩展了图像自编码器、图像残差网络等图神经网络的优化实现，进一步拓展了卷积神经网络类的新型高效操作。