SyNGLER generates synthetic networks by reconstructing latent embeddings with a distribution-free generator over learned node embeddings from latent space models, with consistency guarantees on edge distributions and better preservation of network moments and degrees than prior methods.
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Spectral bounds relate graph Laplacian eigenvalues to the congestion of binary-tree embeddings, with an efficient spectral-ordering algorithm and applications to tensor-network contraction complexity.
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Efficient Synthetic Network Generation via Latent Embedding Reconstruction
SyNGLER generates synthetic networks by reconstructing latent embeddings with a distribution-free generator over learned node embeddings from latent space models, with consistency guarantees on edge distributions and better preservation of network moments and degrees than prior methods.
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Congestion bounds via Laplacian eigenvalues and their application to tensor networks with arbitrary geometry
Spectral bounds relate graph Laplacian eigenvalues to the congestion of binary-tree embeddings, with an efficient spectral-ordering algorithm and applications to tensor-network contraction complexity.