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。

无概念因果分解变分图自编码器

Concept-free Causal Disentanglement with Variational Graph Auto-Encoder

This work studies the use of visual semantic representations to align
entities in heterogeneous knowledge graphs (KGs). Images are natural components
of many existing KGs. By combining visual knowledge with other auxiliary
information, we show that the proposed new approach, EVA, creates a holistic
entity representation that provides strong signals for cross-graph entity
alignment. Besides, previous entity alignment methods require human labelled
seed alignment, restricting availability. EVA provides a completely
unsupervised solution by leveraging the visual similarity of entities to create
an initial seed dictionary (visual pivots). Experiments on benchmark data sets
DBP15k and DWY15k show that EVA offers state-of-the-art performance on both
monolingual and cross-lingual entity alignment tasks. Furthermore, we discover
that images are particularly useful to align long-tail KG entities, which
inherently lack the structural contexts necessary for capturing the
correspondences.

本文介绍了一种新的方法 EVA，通过图像与其他辅助信息相结合，利用视觉相似度来创建一个初始种子词典，从而提供了一个完全无监督的实体对齐的解决方案。实验结果表明，该方法在单语和跨语言实体对齐任务上均能提供最先进的性能。此外，我们发现图像对于对齐长尾 KG 实体尤其有用。

用于 (无监督) 实体对齐的视觉枢轴

Visual Pivoting for (Unsupervised) Entity Alignment

In this paper, we propose a generic neural-based hair rendering pipeline that
can synthesize photo-realistic images from virtual 3D hair models. Unlike
existing supervised translation methods that require model-level similarity to
preserve consistent structure representation for both real images and fake
renderings, our method adopts an unsupervised solution to work on arbitrary
hair models. The key component of our method is a shared latent space to encode
appearance-invariant structure information of both domains, which generates
realistic renderings conditioned by extra appearance inputs. This is achieved
by domain-specific pre-disentangled structure representation, partially shared
domain encoder layers and a structure discriminator. We also propose a simple
yet effective temporal conditioning method to enforce consistency for video
sequence generation. We demonstrate the superiority of our method by testing it
on a large number of portraits and comparing it with alternative baselines and
state-of-the-art unsupervised image translation methods.

提出一种使用神经网络的头发渲染流程，通过共享潜在空间编码两个领域的外观不变结构信息生成逼真的渲染图像，并使用简单而有效的时间条件方法，可以在未经监督的情况下工作于任意头发模型，并在大量肖像上测试，得出其优于替代方案和最先进的无监督图像翻译方法的结论。