AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes a principled augmentation strategy based on degree-preserving edge rewiring, inspired by the configuration model, that generates alternative graph views that maintain node degrees while randomizing the graph topology.
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.
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 show that incorporating NC-LID-based regularization consistently improves reconstruction performance over the baseline without structural regularization and the method using hub-aware regularization, which highlights NC-LID as a useful structural signal for enhancing distance-based graph autoencoders in dyn...
This work introduces a novel method Schreier-Coset Graph Rewiring, a group-theoretic rewiring method that augments the input graph with a Schreier-Coset graph derived from a special linear group, creating a low-resistance bypass for long-range communication.
A Hypergraph-enhanced graph contrastive learning framework for Graph Out-Of-Distribution detection (termed HGOOD), which constructs two branches to hierarchically mine graph compact semantics in a comprehensive manner and introduces a cross-branch prototype contrast that aligns the captured graph patterns with their cr...
Similarity-guided Structural Matching Learning for Graph Dataset Condensation (SSGDC) is proposed, which efficiently reduces repository size while maintaining both task performance and structural information.
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