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.

提出了一种无监督学习的解决方案，称为无概念因果分解 (disentanglement)，通过在可观测数据中直接学习概念结构，提出了 CCVGAE 和 CC-Meta-Graph 模型，并在 AUC 方面分别取得了高达 29% 和 11% 的绝对改进。