Graph representation learning is a fundamental research issue in various
domains of applications, of which the inductive learning problem is
particularly challenging as it requires models to generalize to unseen graph
structures during inference. In recent years, graph neural networks (GNNs) have
emerged as powerful graph models for inductive learning tasks such as node
classification, whereas they typically heavily rely on the annotated nodes
under a fully supervised training setting. Compared with the GNN-based methods,
variational graph auto-encoders (VGAEs) are known to be more generalizable to
capture the internal structural information of graphs independent of node
labels and have achieved prominent performance on multiple unsupervised
learning tasks. However, so far there is still a lack of work focusing on
leveraging the VGAE framework for inductive learning, due to the difficulties
in training the model in a supervised manner and avoiding over-fitting the
proximity information of graphs. To solve these problems and improve the model
performance of VGAEs for inductive graph representation learning, in this work,
we propose the Self-Label Augmented VGAE model. To leverage the label
information for training, our model takes node labels as one-hot encoded inputs
and then performs label reconstruction in model training. To overcome the
scarcity problem of node labels for semi-supervised settings, we further
propose the Self-Label Augmentation Method (SLAM), which uses pseudo labels
generated by our model with a node-wise masking approach to enhance the label
information. Experiments on benchmark inductive learning graph datasets verify
that our proposed model archives promising results on node classification with
particular superiority under semi-supervised learning settings.

提出了自标签增强的 VGAE 模型，包括自标签增强方法（SLAM），用于图的归纳学习，通过在模型训练中进行标签重建以利用标签信息，并采用节点遮盖方法生成伪标签来增强标签信息。实验证明在半监督学习设置下，我们的模型在节点分类上表现出明显的优势。

基于变分图自编码器的归纳学习方法用于半监督分类

Variational Graph Auto-Encoder Based Inductive Learning Method for  Semi-Supervised Classification

Variational Graph Auto-Encoders (VGAEs) have been widely used to solve the
node clustering task. However, the state-of-the-art methods have numerous
challenges. First, existing VGAEs do not account for the discrepancy between
the inference and generative models after incorporating the clustering
inductive bias. Second, current models are prone to degenerate solutions that
make the latent codes match the prior independently of the input signal (i.e.,
Posterior Collapse). Third, existing VGAEs overlook the effect of the noisy
clustering assignments (i.e., Feature Randomness) and the impact of the strong
trade-off between clustering and reconstruction (i.e., Feature Drift). To
address these problems, we formulate a variational lower bound in a contrastive
setting. Our lower bound is a tighter approximation of the log-likelihood
function than the corresponding Evidence Lower BOund (ELBO). Thanks to a newly
identified term, our lower bound can escape Posterior Collapse and has more
flexibility to account for the difference between the inference and generative
models. Additionally, our solution has two mechanisms to control the trade-off
between Feature Randomness and Feature Drift. Extensive experiments show that
the proposed method achieves state-of-the-art clustering results on several
datasets. We provide strong evidence that this improvement is attributed to
four aspects: integrating contrastive learning and alleviating Feature
Randomness, Feature Drift, and Posterior Collapse.

改进了 Variational Graph Auto-Encoders 方法以应对节点聚类任务中的挑战，包括推理和生成模型的差异、后验坍缩、特征随机性和特征漂移，并通过对比学习获得更好的聚类结果。

一个用于节点聚类的对比变分图自编码器

A Contrastive Variational Graph Auto-Encoder for Node Clustering

Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand
the flexibility of variational graph auto-encoders (VGAE) to model graph data.
SIG-VAE employs a hierarchical variational framework to enable neighboring node
sharing for better generative modeling of graph dependency structure, together
with a Bernoulli-Poisson link decoder. Not only does this hierarchical
construction provide a more flexible generative graph model to better capture
real-world graph properties, but also does SIG-VAE naturally lead to
semi-implicit hierarchical variational inference that allows faithful modeling
of implicit posteriors of given graph data, which may exhibit heavy tails,
multiple modes, skewness, and rich dependency structures. Compared to VGAE, the
derived graph latent representations by SIG-VAE are more interpretable, due to
more expressive generative model and more faithful inference enabled by the
flexible semi-implicit construction. Extensive experiments with a variety of
graph data show that SIG-VAE significantly outperforms state-of-the-art methods
on several different graph analytic tasks.

该论文提出了一种名为 Semi-implicit graph variational auto-encoder（SIG-VAE）的模型，采用分层变分框架，建立了一种更灵活的生成图模型，用于更好地捕捉现实世界的图形属性，并能够准确建模给定图形数据的后验概率分布。