A Network Science Perspective on Evaluating Deep Graph Generative Models
A paper evaluates deep graph generative models and the configuration model from a network science perspective, assessing topological similarity to real-world networks and utility in identifying node immunization strategies to suppress epidemic/misinformation spreading. Two deep graph generative models produced synthetic networks closely resembling real-world structural properties and enabled effective immunization strategy identification.
The paper, published on arXiv, compares deep graph generative models with traditional network models like the configuration model. It finds that two deep graph generative models generate synthetic networks that closely match real-world network structural properties and are useful for identifying effective node immunization strategies against epidemic or misinformation spread.
The evaluation focuses on topological similarity and downstream utility for immunization, indicating that deep graph generative models can capture complex structural distributions beyond selected topological properties. The success of two models suggests advances in graph neural network architectures for realistic network synthesis.
Synthetic network generation is critical for privacy-preserving sharing of social contact networks, enabling epidemic mitigation research without exposing real data. This work highlights the practical value of deep graph generative models in public health and network analysis.
Deep graph generative models offer a way to create realistic synthetic networks for testing interventions, potentially reducing reliance on sensitive real data and accelerating research in epidemiology and misinformation control.
Future work may extend evaluation to more diverse real-world networks and additional downstream tasks, and could lead to improved generative models that balance realism and utility for specific applications like immunization strategy design.