Graph Pyramid Pooling Transformer for Graph-Level Representation Learning

ADC-GNN: Adaptive Dual-level Collaborative Graph Neural Networks for Graph Classification

This work proposes a novel Adaptive Dual-level Collaborative GNN associated with an adaptive dual-level collaborative mechanism, and shows that the ADC-GNN can inject the learned high-level information back into the node level, forming a closed-loop, bidirectional optimization process.

Two-level domain-decomposition AdaGrad method for scalable training of graph neural networks

The proposed DD-AG2m alternates between AG2m optimization on the original (global) graph and AG2m optimization on the partitioned graphs, and introduces a two-level variant that performs global optimization steps on a coarse graph obtained by randomly subsampling nodes within each subdomain.

Geometric Flow enhanced Graph Coarsening

This study introduces discrete graph curvature, particularly Ollivier-Ricci curvature, as a measure of higher-order connectivity around an edge, and introduces a novel graph pooling method named RicciPool, utilizing the spectral clustering technique to learn a new cluster assignment matrix.

MvCConvFormer: The graph convolution transformer network with self-distillation for multi-view graph clustering

Stability-Shaped Deep Graph Learning

In deep graph neural networks, increasing depth enlarges the receptive field but often leads to over-smoothing, where node representations tend to align. We develop a unified, mode-wise stability framework for deep GNN propagation that provides a principled characterization of over-smoothing. By interpreting layer dept...

RCGELM-AE: an efficient graph embedding deep model without gradient descent

This study proposes a model called the Residual compensation graph Convolutional Generalized Extreme Learning Machine Autoencoder (RCGELM-AE), which is built on the Extreme Learning Machine Autoencoder (ELM-AE) and incorporates two key designs, and offers a novel, gradient-descent-free solution for achieving efficient...