发表机构
Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究从网络科学视角评估深度图生成模型与配置模型，发现两种深度图生成模型生成的合成网络与真实网络结构属性高度相似，可用于识别有效免疫策略。
AI 中文摘要
来自网络科学的传统网络模型，如Erdos-Renyi模型和配置模型，生成的随机网络仅能复现真实网络中少数选定的拓扑属性。深度图生成模型作为一种数据驱动方法应运而生，它利用深度神经网络架构直接从真实网络中学习复杂的结构分布，以生成更逼真的合成网络。由于真实社交接触网络因隐私风险无法共享，合成网络成为开发和评估疫情缓解策略的替代方案。本研究从网络科学视角评估深度图生成模型和配置模型，既评估生成网络与真实网络之间的拓扑相似性，也评估它们在识别有效节点免疫策略以抑制疫情或错误信息传播方面的效用。研究发现，两种深度图生成模型生成的合成网络与真实网络的结构属性高度相似，使其能够识别出有效的免疫策略。
英文摘要
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.
