In disentangled representation learning, the goal is to achieve a compact representation that consists of all interpretable generative factors in the observational data. Learning disentangled representations for graphs becomes increasingly important as graph data rapidly grows. Existing approaches often rely on Variational Auto-Encoder (VAE) or its causal structure learning-based refinement, which suffer from sub-optimality in VAEs due to the independence factor assumption and unavailability of concept labels, respectively. In this paper, we propose an unsupervised solution, dubbed concept-free causal disentanglement, built on a theoretically provable tight upper bound approximating the optimal factor. This results in an SCM-like causal structure modeling that directly learns concept structures from data. Based on this idea, we propose Concept-free Causal VGAE (CCVGAE) by incorporating a novel causal disentanglement layer into Variational Graph Auto-Encoder. Furthermore, we prove concept consistency under our concept-free causal disentanglement framework, hence employing it to enhance the meta-learning framework, called concept-free causal Meta-Graph (CC-Meta-Graph). We conduct extensive experiments to demonstrate the superiority of the proposed models: CCVGAE and CC-Meta-Graph, reaching up to $29\%$ and $11\%$ absolute improvements over baselines in terms of AUC, respectively.

翻译：在解缠表示学习中，目标是通过观测数据获得包含所有可解释生成因子的紧凑表示。随着图数据的快速增长，学习图的解缠表示变得日益重要。现有方法通常依赖变分自编码器（VAE）或其基于因果结构学习的改进，但前者因独立因子假设、后者因概念标签缺失而各自存在次优性。本文提出一种名为无概念因果解缠的无监督解决方案，该方案基于理论上可证明的、逼近最优因子的紧密上界，从而构建类似结构方程模型（SCM）的因果结构建模方法，直接从数据中学习概念结构。基于此思想，我们通过将新颖的因果解缠层引入变分图自编码器，提出了无概念因果变分图自编码器（CCVGAE）。进一步，我们在无概念因果解缠框架下证明了概念一致性，并将其用于增强元学习框架，提出无概念因果元图（CC-Meta-Graph）。大量实验表明，所提模型CCVGAE和CC-Meta-Graph具有优越性：在AUC指标上分别比基线方法绝对提升高达29%和11%。