# Graph Inference Learning for Semi-supervised Classification

arXiv: https://arxiv.org/abs/2001.06137 · HTML: https://mlsanity.com/paper/2001.06137/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt](https://mlsanity.com/paper/2310.14845/) (2023)
- [Graph Inference Representation: Learning Graph Positional Embeddings with Anchor Path Encoding](https://mlsanity.com/paper/2105.03821/) (2021)
- [Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks](https://mlsanity.com/paper/2205.03753/) (2022)

## Abstract (arXiv)

In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Recent works often solve this problem via advanced graph convolution in a conventionally supervised manner, but the performance could degrade significantly when labeled data is scarce. To this end, we propose a Graph Inference Learning (GIL) framework to boost the performance of semi-supervised node classification by learning the inference of node labels on graph topology. To bridge the connection between two nodes, we formally define a structure relation by encapsulating node attributes, between-node paths, and local topological structures together, which can make the inference conveniently deduced from one node to another node. For learning the inference process, we further introduce meta-optimization on structure relations from training nodes to validation nodes, such that the learnt graph inference capability can be better self-adapted to testing nodes. Comprehensive evaluations on four benchmark datasets (including Cora, Citeseer, Pubmed, and NELL) demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods on the semi-supervised node classification task.