# Semi-Implicit Graph Variational Auto-Encoders

arXiv: https://arxiv.org/abs/1908.07078 · HTML: https://mlsanity.com/paper/1908.07078/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 30 of 54)

- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation](https://mlsanity.com/paper/2404.04057/) (2024)
- [Self-Supervised Learning of Graph Neural Networks: A Unified Review](https://mlsanity.com/paper/2102.10757/) (2021)
- [Graph Prompt Learning: A Comprehensive Survey and Beyond](https://mlsanity.com/paper/2311.16534/) (2023)
- [A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications](https://mlsanity.com/paper/2202.07893/) (2022)
- [SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking](https://mlsanity.com/paper/2301.12458/) (2023)
- [Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction](https://mlsanity.com/paper/2202.00961/) (2022)
- [MUSE: Multi-View Contrastive Learning for Heterophilic Graphs](https://mlsanity.com/paper/2307.16026/) (2023)
- [Graph Diffusion Policy Optimization](https://mlsanity.com/paper/2402.16302/) (2024)
- [Advancing Graph Generation through Beta Diffusion](https://mlsanity.com/paper/2406.09357/) (2024)
- [Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders](https://mlsanity.com/paper/2108.01053/) (2021)
- [Masked Graph Autoencoder with Non-discrete Bandwidths](https://mlsanity.com/paper/2402.03814/) (2024)
- [A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective](https://mlsanity.com/paper/2403.16137/) (2024)
- [Mesh-based graph convolutional neural networks for modeling materials with microstructure](https://mlsanity.com/paper/2107.00090/) (2021)
- [Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders](https://mlsanity.com/paper/2404.15806/) (2024)
- [Exploring Correlations of Self-Supervised Tasks for Graphs](https://mlsanity.com/paper/2405.04245/) (2024)
- [A Variational Edge Partition Model for Supervised Graph Representation Learning](https://mlsanity.com/paper/2202.03233/) (2022)
- [ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network](https://mlsanity.com/paper/2312.05736/) (2023)
- [Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code Selection](https://mlsanity.com/paper/2504.12715/) (2025)
- [A Contrastive Variational Graph Auto-Encoder for Node Clustering](https://mlsanity.com/paper/2312.16830/) (2023)
- [Uncertainty Quantification on Graph Learning: A Survey](https://mlsanity.com/paper/2404.14642/) (2024)
- [Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings](https://mlsanity.com/paper/2409.10452/) (2024)
- [MoReL: Multi-omics Relational Learning](https://mlsanity.com/paper/2203.08149/) (2022)
- [Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2207.03332/) (2022)
- [Interpretable Node Representation with Attribute Decoding](https://mlsanity.com/paper/2212.01682/) (2022)
- [Leverage Variational Graph Representation For Model Poisoning on Federated Learning](https://mlsanity.com/paper/2404.15042/) (2024)
- [Uncertainty-Aware Deep Neural Representations for Visual Analysis of Vector Field Data](https://mlsanity.com/paper/2407.16119/) (2024)
- [Uncertainty-Informed Volume Visualization using Implicit Neural Representation](https://mlsanity.com/paper/2408.06018/) (2024)
- [Graph Representation Learning via Contrasting Cluster Assignments](https://mlsanity.com/paper/2112.07934/) (2021)
- [GANN: Graph Alignment Neural Network for Semi-Supervised Learning](https://mlsanity.com/paper/2303.07778/) (2023)

## Abstract (arXiv)

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.