519 citations · 1.1k across the 13 of their papers we have counts for
13 papers
Full-Spectrum Graph Neural Networks: Expressive and Scalable
Xiaohan Wang, Deyu Bo, Longlong Li +1
It is well established that spectral graph neural networks (GNNs) can universally approximate node signals; however, their expressive power remains bounded by the 1-dimensional Wei…
Graph-GRPO: Training Graph Flow Models with Reinforcement Learning
Baoheng Zhu, Deyu Bo, Delvin Ce Zhang +1
Graph generation is a fundamental task with broad applications, such as drug discovery. Recently, discrete flow matching-based graph generation, \aka, graph flow model (GFM), has e…
Graph Positional Autoencoders as Self-supervised Learners
Yang Liu, Deyu Bo, Wenxuan Cao +3
Graph self-supervised learning seeks to learn effective graph representations without relying on labeled data. Among various approaches, graph autoencoders (GAEs) have gained signi…
Understanding Dataset Distillation via Spectral Filtering
Deyu Bo, Songhua Liu, Xinchao Wang
Dataset distillation (DD) has emerged as a promising approach to compress datasets and speed up model training. However, the underlying connections among various DD methods remain…
Graph Distillation with Eigenbasis Matching
Yang Liu, Deyu Bo, Chuan Shi
The increasing amount of graph data places requirements on the efficient training of graph neural networks (GNNs). The emerging graph distillation (GD) tackles this challenge by di…
Data-centric Graph Learning: A Survey
Yuxin Guo, Deyu Bo, Cheng Yang +5
The history of artificial intelligence (AI) has witnessed the significant impact of high-quality data on various deep learning models, such as ImageNet for AlexNet and ResNet. Rece…