519 citations · 1.1k across the 6 of their papers we have counts for
7 papers · 1 filter
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…
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
Nian Liu, Xiao Wang, Deyu Bo +2
Graph Contrastive Learning (GCL), learning the node representations by augmenting graphs, has attracted considerable attentions. Despite the proliferation of various graph augmenta…
Beyond Low-frequency Information in Graph Convolutional Networks
Deyu Bo, Xiao Wang, Chuan Shi +1
Graph neural networks (GNNs) have been proven to be effective in various network-related tasks. Most existing GNNs usually exploit the low-frequency signals of node features, which…
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
Xiao Wang, Meiqi Zhu, Deyu Bo +3
Graph Convolutional Networks (GCNs) have gained great popularity in tackling various analytics tasks on graph and network data. However, some recent studies raise concerns about wh…