Policy-Embedded Graph Expansion: Networked HIV Testing with Diffusion-Driven Network Samples

2026-01-20

AI Summary

This study addresses the unrealistic assumption of complete network structure in existing social-network-based sequential HIV testing strategies. To overcome this limitation, the authors propose the Policy-Embedded Graph Expansion (PEGE) framework, which uniquely integrates the graph expansion process directly into the sequential decision-making policy, thereby circumventing the need for explicit network topology reconstruction. Furthermore, they introduce a Dynamics-Driven Branching (DDB) diffusion model tailored to the sparse, forest-like referral structures observed in real-world settings. Experimental results on empirical HIV transmission networks demonstrate that the PEGE+DDB approach achieves a 13% improvement in discounted reward over baseline methods and identifies 9% more infected individuals when testing 25% of the population.

Technology Category

• Search and Optimization: Heuristic Search
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

HIV is a retrovirus that attacks the human immune system and can lead to death without proper treatment. In collaboration with the WHO and Wits University, we study how to improve the efficiency of HIV testing with the goal of eventual deployment, directly supporting progress toward UN Sustainable Development Goal 3.3. While prior work has demonstrated the promise of intelligent algorithms for sequential, network-based HIV testing, existing approaches rely on assumptions that are impractical in our real-world implementations. Here, we study sequential testing on incrementally revealed disease networks and introduce Policy-Embedded Graph Expansion (PEGE), a novel framework that directly embeds a generative distribution over graph expansions into the decision-making policy rather than attempting explicit topological reconstruction. We further propose Dynamics-Driven Branching (DDB), a diffusion-based graph expansion model that supports decision making in PEGE and is designed for data-limited settings where forest structures arise naturally, as in our real-world referral process. Experiments on real HIV transmission networks show that the combined approach (PEGE + DDB) consistently outperforms existing baselines (e.g., 13% improvement in discounted reward and 9% more HIV detections with 25% of the population tested) and explore key tradeoffs that drive decision quality.

Problem

• HIV testing
• networked testing
• sequential testing
• graph expansion
• data-limited settings

Innovation

• Policy-Embedded Graph Expansion
• Dynamics-Driven Branching
• network-based HIV testing
• diffusion-driven graph expansion
• sequential decision making