1 citations · 2 across the 7 of their papers we have counts for
5 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…
Rhomboid Tiling for Geometric Graph Deep Learning
Yipeng Zhang, Longlong Li, Kelin Xia
Graph Neural Networks (GNNs) have proven effective for learning from graph-structured data through their neighborhood-based message passing framework. Many hierarchical graph clust…
Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing
Longlong Li, Mengyang Zhao, Guanghui Wang +1
Most Graph Neural Networks (GNNs) propagate messages by treating node embeddings as holistic feature vectors, implicitly assuming uniform relevance across feature dimensions. This…
TGT: A Temporal Gating Transformer for Smartphone App Usage Prediction
Longlong Li, Cunquan Qu, Guanghui Wang
Accurately predicting smartphone app usage is challenging due to the sparsity and irregularity of user behavior, especially under cold-start and low-activity conditions. Existing a…
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction
Longlong Li, Yipeng Zhang, Guanghui Wang +1
As key models in geometric deep learning, graph neural networks have demonstrated enormous power in molecular data analysis. Recently, a specially-designed learning scheme, known a…