AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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HGKAN (Hyperbolic Graph-Based Kolmogorov–Arnold Network), a unified framework that combines hyperbolic representation learning, graph neural networks, and KAN-based nonlinear fusion for social recommendation, is proposed.
The results demonstrate the value of combining graph transformers with hyperbolic space modeling and the gated fusion for next-item recommendation in sequential and social settings (the authors' code).
GCTMR (Graph Contrastive Transformer for Multibehavior Recommendation), a framework that integrates graph-based contrastive learning with a behavior-aware Transformer architecture, is proposed, with particularly remarkable performance under sparse data conditions where traditional methods struggle most.
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