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Found 7 papers, 3 papers with code
CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Model
Accurate, and effective traffic forecasting is vital for smart traffic systems, crucial in urban traffic planning and management.
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond
To explore more flexible filtering conditions, we further generalize MHKG into a model termed G-MHKG and thoroughly show the roles of each element in controlling over-smoothing, over-squashing and expressive power.
How Curvature Enhance the Adaptation Power of Framelet GCNs
Motivated by the geometric analogy of Ricci curvature in the graph setting, we prove that by inserting the curvature information with different carefully designed transformation function $\zeta$, several known computational issues in GNN such as over-smoothing can be alleviated in our proposed model.
Frameless Graph Knowledge Distillation
Knowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be accomplished efficiently and without losing too much prediction accuracy.
Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and Training with Non-Linear Diffusion
We conduct a convergence analysis on pL-UFG, addressing the gap in the understanding of its asymptotic behaviors.
Generalized Laplacian Regularized Framelet Graph Neural Networks
This paper introduces a novel Framelet Graph approach based on p-Laplacian GNN.
Generalized energy and gradient flow via graph framelets
In this work, we provide a theoretical understanding of the framelet-based graph neural networks through the perspective of energy gradient flow.
