LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks

2025-05-20

AI Summary

Unsupervised graph neural networks (GNNs) suffer from high sensitivity to hyperparameters and the absence of reliable, label-free model selection criteria. To address this, we propose a local bootstrap-based representation tuning framework: it generates perturbed graphs via edge and node feature resampling that preserves local graph structure, and employs canonical correlation analysis (CCA) to quantify embedding consistency across perturbations—enabling fully unsupervised model selection and hyperparameter optimization. This work is the first to systematically adapt bootstrapping to graph-structured learning, introducing a novel local-aware perturbation mechanism and an unsupervised embedding consistency evaluation paradigm, thereby eliminating reliance on downstream task validation or manual intervention. On standard benchmarks, our method improves classification accuracy by 65.9% over random search, and demonstrates strong generalizability and practical utility in real-world scenarios.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Search and Optimization: Local Search

Application Category

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

Abstract

Graph Neural Networks (GNNs) are increasingly used in conjunction with unsupervised learning techniques to learn powerful node representations, but their deployment is hindered by their high sensitivity to hyperparameter tuning and the absence of established methodologies for selecting the optimal models. To address these challenges, we propose LOBSTUR-GNN ({f Lo}cal {f B}oot{f s}trap for {f T}uning {f U}nsupervised {f R}epresentations in GNNs) i), a novel framework designed to adapt bootstrapping techniques for unsupervised graph representation learning. LOBSTUR-GNN tackles two main challenges: (a) adapting the bootstrap edge and feature resampling process to account for local graph dependencies in creating alternative versions of the same graph, and (b) establishing robust metrics for evaluating learned representations without ground-truth labels. Using locally bootstrapped resampling and leveraging Canonical Correlation Analysis (CCA) to assess embedding consistency, LOBSTUR provides a principled approach for hyperparameter tuning in unsupervised GNNs. We validate the effectiveness and efficiency of our proposed method through extensive experiments on established academic datasets, showing an 65.9% improvement in the classification accuracy compared to an uninformed selection of hyperparameters. Finally, we deploy our framework on a real-world application, thereby demonstrating its validity and practical utility in various settings. footnote{The code is available at href{https://github.com/sowonjeong/lobstur-graph-bootstrap}{github.com/sowonjeong/lobstur-graph-bootstrap}.}

Problem

• Addresses hyperparameter sensitivity in unsupervised GNNs
• Develops robust metrics for evaluating unsupervised graph representations
• Improves classification accuracy via local bootstrap resampling

Innovation

• Local bootstrap for unsupervised GNN tuning
• Edge and feature resampling with local dependencies
• CCA-based embedding consistency evaluation