The existing graph neural architecture search (GNAS) methods heavily rely on
supervised labels during the search process, failing to handle ubiquitous
scenarios where supervisions are not available. In this paper, we study the
problem of unsupervised graph neural architecture search, which remains
unexplored in the literature. The key problem is to discover the latent graph
factors that drive the formation of graph data as well as the underlying
relations between the factors and the optimal neural architectures. Handling
this problem is challenging given that the latent graph factors together with
architectures are highly entangled due to the nature of the graph and the
complexity of the neural architecture search process. To address the challenge,
we propose a novel Disentangled Self-supervised Graph Neural Architecture
Search (DSGAS) model, which is able to discover the optimal architectures
capturing various latent graph factors in a self-supervised fashion based on
unlabeled graph data. Specifically, we first design a disentangled graph
super-network capable of incorporating multiple architectures with factor-wise
disentanglement, which are optimized simultaneously. Then, we estimate the
performance of architectures under different factors by our proposed
self-supervised training with joint architecture-graph disentanglement.
Finally, we propose a contrastive search with architecture augmentations to
discover architectures with factor-specific expertise. Extensive experiments on
11 real-world datasets demonstrate that the proposed model is able to achieve
state-of-the-art performance against several baseline methods in an
unsupervised manner.

通过提出一种新颖的解缠结自监督图神经结构搜索 (DSGAS) 模型，并在未标记的图数据上进行自监督训练，该模型可以以无监督的方式发现捕捉各种潜在图因素的最佳结构，并取得在一些基准方法上的最先进性能。