AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
Discrete diffusion models are a prominent family for graph generation, but standard class-conditional mechanisms embed the class signal in the denoiser during training, tying the conditioning mechanism to the trained model. Classifier guidance avoids this coupling in continuous domains by steering a frozen model with a...
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.
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