Graph-based Semi-Supervised Learning via Maximum Discrimination

2026-02-08

AI Summary

This work addresses the limitations of conventional graph-based semi-supervised learning methods in scenarios with scarce labeled data but abundant unlabeled data, particularly under complex label distributions. The authors propose AUC-spec, a novel approach that, for the first time, integrates AUC maximization into the graph semi-supervised learning framework. By jointly optimizing inter-class separability and graph smoothness through low-dimensional embeddings, the method achieves a principled balance between discriminative power and structural consistency. Leveraging a manifold product model, the theoretical analysis establishes a polynomial dependence of the required labeled sample size on key problem parameters. Extensive experiments on both synthetic and real-world datasets demonstrate that AUC-spec delivers competitive classification performance while maintaining computational efficiency, effectively reconciling class separation with graph-structure fidelity.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Semi-supervised learning (SSL) addresses the critical challenge of training accurate models when labeled data is scarce but unlabeled data is abundant. Graph-based SSL (GSSL) has emerged as a popular framework that captures data structure through graph representations. Classic graph SSL methods, such as Label Propagation and Label Spreading, aim to compute low-dimensional representations where points with the same labels are close in representation space. Although often effective, these methods can be suboptimal on data with complex label distributions. In our work, we develop AUC-spec, a graph approach that computes a low-dimensional representation that maximizes class separation. We compute this representation by optimizing the Area Under the ROC Curve (AUC) as estimated via the labeled points. We provide a detailed analysis of our approach under a product-of-manifold model, and show that the required number of labeled points for AUC-spec is polynomial in the model parameters. Empirically, we show that AUC-spec balances class separation with graph smoothness. It demonstrates competitive results on synthetic and real-world datasets while maintaining computational efficiency comparable to the field's classic and state-of-the-art methods.

Problem

• Semi-Supervised Learning
• Graph-based Learning
• Class Separation
• Label Scarcity
• Complex Label Distributions

Innovation

• Graph-based Semi-Supervised Learning
• AUC Optimization
• Maximum Discrimination
• Label Efficiency
• Manifold Model