Abstract
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.
Abstract (translated)
现有的图神经网络架构搜索（GNAS）方法在搜索过程中严重依赖有监督标签，在无法获得监督时无法处理普遍场景。在本文中，我们研究了无监督图神经网络架构搜索问题，该问题在文献中尚未被深入研究。关键问题是要发现推动图数据形成的关键隐式图因素以及因素之间的底层关系。由于图的性质和神经网络架构搜索过程的复杂性，解决这个问题具有挑战性。为了解决这个问题，我们提出了一个新颖的剥离式自监督图神经网络架构搜索（DSGAS）模型，它能够以自监督的方式基于无监督图数据发现最优架构，捕捉各种隐式图因素。具体来说，我们首先设计了一个剥离的图超网络，具有多个架构因子水平的剥离，这些因子同时优化。然后，我们通过自监督训练与架构-图剥离估计架构的性能。最后，我们提出了一个对比性搜索与架构增强来发现具有特定专业知识因素的架构。在11个真实世界数据集上进行广泛的实验证明，与几个基线方法相比，该模型可以在无需监督的情况下实现最先进的性能。
URL
https://arxiv.org/abs/2403.05064
