70 citations · 139 across the 10 of their papers we have counts for
10 papers
GD4: Graph-based Discrete Denoising Diffusion for MIMO Detection
Qincheng Lu, Sitao Luan, Xiao-Wen Chang
In wireless communications, recovering the optimal solution to the multiple-input multiple-output (MIMO) detection problem is NP-hard. Obtaining high-quality suboptimal solutions w…
Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning
Qincheng Lu, Jiaqi Zhu, Sitao Luan +1
The ability of Graph Neural Networks (GNNs) to capture long-range and global topology information is limited by the scope of conventional graph Laplacian, leading to unsatisfactory…
Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning
Sitao Luan, Qincheng Lu, Chenqing Hua +3
Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data. However, recent studies have found that heterophily c…
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Sitao Luan, Chenqing Hua, Qincheng Lu +11
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of…
GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection
Qincheng Lu, Sitao Luan, Xiao-Wen Chang
Massive MIMO (multiple-input multiple-output) detection is an important topic in wireless communication and various machine learning based methods have been developed recently for…
Representation Learning on Heterophilic Graph with Directional Neighborhood Attention
Qincheng Lu, Jiaqi Zhu, Sitao Luan +1
Graph Attention Network (GAT) is one of the most popular Graph Neural Network (GNN) architecture, which employs the attention mechanism to learn edge weights and has demonstrated p…
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability
Sitao Luan, Chenqing Hua, Minkai Xu +6
Homophily principle, i.e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Netwo…
Revisiting Heterophily For Graph Neural Networks
Sitao Luan, Chenqing Hua, Qincheng Lu +5
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption). While GNNs have been common…
When Do We Need Graph Neural Networks for Node Classification?
Sitao Luan, Chenqing Hua, Qincheng Lu +3
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treati…
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
Sitao Luan, Chenqing Hua, Qincheng Lu +5
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using the graph structures based on the relational inductive bias (homophily assumption). Though GNNs are believe…