Semi-Supervised Learning on Graphs using Graph Neural Networks
New 57-page study provides the first rigorous mathematical proof for GNN performance in semi-supervised learning.
Researchers Juntong Chen, Claire Donnat, Olga Klopp, and Johannes Schmidt-Hieber published a foundational paper titled 'Semi-Supervised Learning on Graphs using Graph Neural Networks' (arXiv:2602.17115). They proved a sharp non-asymptotic risk bound for GNNs with linear graph convolutions and deep ReLU readouts, separating approximation, stochastic, and optimization errors. This provides the first systematic theoretical framework explaining when and why GNNs succeed with limited labeled data, validated by numerical experiments.
Why It Matters
Provides mathematical certainty for deploying GNNs in critical applications like fraud detection and drug discovery where labeled data is scarce.