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

Recently, researchers have proposed a graph pooling operation, akin to the pooling process in conventional convolutional neural networks (CNN), aimed at reducing the computation cost of Graph convolutional neural networks (GCNNs). While most GCNN-based methods treat graph pooling as a node clustering problem and propos...

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