TL;DR
This work introduces a graph generation method that incorporates graph-theoretic principles into the learning process and preserves both global and local characteristics of the input graph while correcting the degree distribution to avoid duplicating the original topology.
Experiments on synthetic and real-world datasets show that GraphK outperforms existing methods, accurately learns graph structures, and generates synthetic graphs without explicit definitions.
This work shows that informative embeddings can be derived without complicated model design and gradient-based training, and suggests that informative graph embeddings can arise from carefully chosen topological transformations before any learning operation is applied.
ProtoGuide is introduced, a post-hoc, backbone-agnostic framework that recovers an analogous mechanism for discrete diffusion models and raises macro classification accuracy and outperforms DiGress's built-in conditional training under the authors' configuration.
The robustness of shallow graph embedding methods for community detection in the face of network perturbations is found to be influenced by factors such as network size, initial community partition strength, and the type of perturbation.
GraphVAEBM is introduced, a framework that separates graph generation from property modeling, and a Graph Variational Autoencoder is first trained to learn a continuous graph manifold and then frozen, while independent Energy-Based Models are trained to represent specific graph properties.
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