arxiv_ml 95% Match Research Paper Researchers in GNNs,Machine learning practitioners,Data scientists working with graph data 1 week ago

Topology-Aware Active Learning on Graphs

graph-neural-networks › graph-learning

📄 Abstract

Abstract: We propose a graph-topological approach to active learning that directly targets the core challenge of exploration versus exploitation under scarce label budgets. To guide exploration, we introduce a coreset construction algorithm based on Balanced Forman Curvature (BFC), which selects representative initial labels that reflect the graph's cluster structure. This method includes a data-driven stopping criterion that signals when the graph has been sufficiently explored. We further use BFC to dynamically trigger the shift from exploration to exploitation within active learning routines, replacing hand-tuned heuristics. To improve exploitation, we introduce a localized graph rewiring strategy that efficiently incorporates multiscale information around labeled nodes, enhancing label propagation while preserving sparsity. Experiments on benchmark classification tasks show that our methods consistently outperform existing graph-based semi-supervised baselines at low label rates.

Authors (4)

Harris Hardiman-Mostow

Jack Mauro

Adrien Weihs

Andrea L. Bertozzi

Submitted

October 29, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

Proposes a topology-aware active learning approach for graphs using Balanced Forman Curvature (BFC) to guide exploration and a localized graph rewiring strategy for improved exploitation. BFC selects representative initial labels reflecting cluster structure and dynamically triggers the shift from exploration to exploitation, outperforming existing graph-based semi-supervised baselines.

Business Value

Enables more efficient labeling of graph data, reducing costs and accelerating the development of machine learning models for network analysis, social networks, and molecular structures.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Feasible, as it enhances existing GNN training pipelines.

Limitations Addressed

Inefficient exploration in active learning on graphs,Difficulty in balancing exploration and exploitation,Static heuristics for active learning strategies,Limited ability to incorporate multiscale graph information efficiently

Performance Gains

Consistently outperform existing graph-based semi-supervised baselines at low label rates.

Technical Tags

Active LearningGraph Neural Networks (GNNs)TopologyCoreset ConstructionBalanced Forman Curvature (BFC)Exploration vs ExploitationGraph RewiringLabel Propagation

Research Topics

Active learning on graphsGraph topology for explorationEfficient graph representation learningSemi-supervised learning on graphs

Methods & Architectures

Topology-aware active learningBalanced Forman Curvature (BFC) for coreset constructionData-driven stopping criterionLocalized graph rewiringMultiscale information integration Graph Neural Networks (GNNs)

Applications & Tasks

Graph Data Analysis Machine Learning Network Analysis Exploration vs exploitation trade-off in active learningLimited label budgetsCapturing graph structure for efficient learningDynamically shifting between exploration and exploitation Improving active learning efficiency on graphsSelecting informative nodes for labelingEnhancing label propagation in GNNs

Datasets & Benchmarks

Benchmarks

Benchmark classification tasks

Related Fields

Graph Neural NetworksActive LearningSemi-Supervised LearningMachine Learning TheoryNetwork ScienceTopological Data Analysis

Keywords

Graph Neural NetworksActive LearningTopologyCoresetBalanced Forman CurvatureExplorationExploitationGraph RewiringLabel PropagationSemi-Supervised LearningNetwork AnalysisGraph Representation Learning

Academic Context

#Active learning on graphs#Graph topology for exploration#Efficient graph representation learning#Semi-supervised learning on graphs

Commercial Potential

Potential Products

Active learning tools for graph datasetsGNN libraries with enhanced active learning modules

Target Industries

Social MediaE-commerceBiotechnologyCybersecurityTelecommunications

Use Case Examples

Efficiently labeling nodes in a social network for community detectionIdentifying key molecules in a biological networkDetecting fraudulent transactions in financial networks

Competitive Edge

Offers a principled, topology-driven approach to active learning on graphs that dynamically balances exploration and exploitation, outperforming methods relying on static heuristics or less informative node selection criteria.

Market Opportunity

Growing market for graph analytics and GNN applications.

Revenue Models

Licensing of active learning algorithmsconsulting services for graph data labeling.

Resource Requirements

Compute Needs

Moderate, depends on graph size and GNN complexity.

Data Requirements

Graph datasets with limited initial labels.

Deployment Constraints

Requires careful tuning of BFC parameters and rewiring strategy based on graph properties.

Scalability

Graph rewiring aims for efficiency, but scalability depends on the specific graph structure and GNN implementation.

Production Readiness

Maturity Level

Research

Time to Market

2-3 years for integration into graph ML platforms.

Patent Potential

Moderate, for the BFC-based coreset construction and dynamic exploration-exploitation strategy.

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