# A Flexible Generative Framework for Graph-based Semi-supervised Learning

arXiv: https://arxiv.org/abs/1905.10769 · HTML: https://mlsanity.com/paper/1905.10769/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 24 of 24)

- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [Data-centric Graph Learning: A Survey](https://mlsanity.com/paper/2310.04987/) (2023)
- [GraphHop: An Enhanced Label Propagation Method for Node Classification](https://mlsanity.com/paper/2101.02326/) (2021)
- [Neural Structured Prediction for Inductive Node Classification](https://mlsanity.com/paper/2204.07524/) (2022)
- [Training on Test Data with Bayesian Adaptation for Covariate Shift](https://mlsanity.com/paper/2109.12746/) (2021)
- [Hyperspectral Image Classification With Contrastive Graph Convolutional Network](https://mlsanity.com/paper/2205.11237/) (2022)
- [Learning on Large Graphs using Intersecting Communities](https://mlsanity.com/paper/2405.20724/) (2024)
- [Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks](https://mlsanity.com/paper/2202.11132/) (2022)
- [Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs](https://mlsanity.com/paper/2401.03597/) (2024)
- [Node Copying: A Random Graph Model for Effective Graph Sampling](https://mlsanity.com/paper/2208.02435/) (2022)
- [Label Efficient Regularization and Propagation for Graph Node Classification](https://mlsanity.com/paper/2204.08646/) (2022)
- [Robust Graph Structure Learning via Multiple Statistical Tests](https://mlsanity.com/paper/2210.03956/) (2022)
- [Diffusion Probabilistic Models for Structured Node Classification](https://mlsanity.com/paper/2302.10506/) (2023)
- [GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networks](https://mlsanity.com/paper/2102.10403/) (2021)
- [Uncertainty Propagation in Node Classification](https://mlsanity.com/paper/2304.00918/) (2023)
- [Unseen Fake News Detection Through Casual Debiasing](https://mlsanity.com/paper/2503.04160/) (2025)
- [Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering](https://mlsanity.com/paper/2111.07942/) (2021)
- [Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs](https://mlsanity.com/paper/2406.16552/) (2024)
- [Customizing Graph Neural Networks using Path Reweighting](https://mlsanity.com/paper/2106.10866/) (2021)
- [Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning](https://mlsanity.com/paper/2407.01886/) (2024)
- [Training Robust Graph Neural Networks by Modeling Noise Dependencies](https://mlsanity.com/paper/2502.19670/) (2025)
- [ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions](https://mlsanity.com/paper/2507.14484/) (2025)
- [GraphPPD: Posterior Predictive Modelling for Graph-Level Inference](https://mlsanity.com/paper/2508.16995/) (2025)
- [Bayesian Neighborhood Adaptation for Graph Neural Networks](https://mlsanity.com/paper/2602.05358/) (2026)

## Abstract (arXiv)

We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-supervised learning tasks. However, conventional graph-based regularization methods and recent graph neural networks do not fully leverage the interrelations between the features, the graph, and the labels. In this work, we propose a flexible generative framework for graph-based semi-supervised learning, which approaches the joint distribution of the node features, labels, and the graph structure. Borrowing insights from random graph models in network science literature, this joint distribution can be instantiated using various distribution families. For the inference of missing labels, we exploit recent advances of scalable variational inference techniques to approximate the Bayesian posterior. We conduct thorough experiments on benchmark datasets for graph-based semi-supervised learning. Results show that the proposed methods outperform the state-of-the-art models in most settings.