Efficient Synthetic Network Generation via Latent Embedding Reconstruction

ArXi:2606.00934v1 Announce Type: cross Network data are ubiquitous across the social sciences, biology, and information systems. Generating realistic synthetic network data has broad applications from network simulation to scientific discovery. However, many existing black-box approaches for network generation tend to overfit observed data while overlooking characteristic network structure, and incur substantial computational overhead at scale.