AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become costly as graphs grow. In this paper, we perform generation explicitly on latent representations of entire graphs obtained from a pretrained Variational Autoencoder with...
This work proposes a method for refining heuristic-obtained clusters by grounding the authors' clustering on contrastive embeddings yielded by graph neural networks, and releases a publicly available dataset of Bitcoin transaction graphs containing a substantial number of clusters.
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