A Network Science Perspective on Evaluating Deep Graph Generative Models

2026-09-01

AI Summary

本文从网络科学角度评估了深度图生成模型，通过比较生成网络与真实网络的拓扑相似性及其在识别有效免疫策略中的效用，解决了合成网络实用性问题。

Abstract

Traditional network models from network science, such as the Erdos-Renyi and configuration models, generate random networks that reproduce few selected topological properties observed in real-world networks. Deep graph generative models emerge as a data-driven approach, leveraging deep neural network architectures to learn complex structural distributions directly from real-world networks to generate more realistic synthetic networks. Because real social contact networks cannot be shared due to privacy risks, synthetic networks serve as an alternative for developing and evaluating epidemic mitigation strategies. In this work, we evaluate deep graph generative models as well as the configuration from a network science perspective by assessing both the topological similarity between generated and real-world networks and their utility in identifying effective node immunization strategies to sup- press epidemic/misinformation spreading. It is found that two deep graph generative models produce synthetic networks that closely resemble the structural properties of real-world networks, enabling them to identify effective immunization strategies.

Problem

• Deep graph generative models
• topological similarity
• synthetic networks
• immunization strategies

Innovation

• deep graph generative models
• topological similarity
• node immunization strategies
• epidemic mitigation