GADPN: Graph Adaptive Denoising and Perturbation Networks via Singular Value Decomposition

A new framework named InfGraND has been introduced to facilitate Influence-guided Knowledge Distillation from Graph Neural Networks (GNNs) to Multi-Layer Perceptrons (MLPs). This framework aims to enhance the efficiency of MLPs by prioritizing structurally influential nodes in the graph, addressing challenges faced by traditional GNNs in low-latency and resource-constrained environments.

Recent research highlights the potential of Subgraph Graph Neural Networks (GNNs) for node classification, addressing the limitations of traditional node-centric approaches that suffer from high computational costs and scalability issues. The proposed SubGND framework aims to enhance efficiency while maintaining classification accuracy through innovative techniques like differentiated zero-padding and Ego-Alter subgraph representation.

A novel framework named Directed Homophily-aware Graph Neural Network (DHGNN) has been introduced to address the challenges faced by traditional Graph Neural Networks (GNNs) in generalizing to heterophilic neighborhoods and in processing directed graphs. DHGNN incorporates homophily-aware and direction-sensitive components, utilizing a resettable gating mechanism and a noise-tolerant fusion module to enhance performance.

A new framework for Gaussian Process (GP) model selection, titled 'The Kernel Manifold', has been introduced, emphasizing a geometric approach to optimize the choice of covariance kernels. This method utilizes a Bayesian optimization framework based on kernel-of-kernels geometry, allowing for efficient exploration of kernel space through expected divergence-based distances.