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Decoupled Graph Neural Networks for Large Dynamic Graphs
Yanping Zheng, Zhewei Wei, Jiajun Liu
Real-world graphs, such as social networks, financial transactions, and recommendation systems, often demonstrate dynamic behavior. This phenomenon, known as graph stream, involves…
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
Mingguo He, Zhewei Wei, Shikun Feng +4
Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-ba…
LON-GNN: Spectral GNNs with Learnable Orthonormal Basis
Qian Tao, Zhen Wang, Wenyuan Yu +2
In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node…
Graph Neural Networks with Learnable and Optimal Polynomial Bases
Yuhe Guo, Zhewei Wei
Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the…
Clenshaw Graph Neural Networks
Yuhe Guo, Zhewei Wei
Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational methods for learning graph representations. Recent GCN m…
Instant Graph Neural Networks for Dynamic Graphs
Yanping Zheng, Hanzhi Wang, Zhewei Wei +2
Graph Neural Networks (GNNs) have been widely used for modeling graph-structured data. With the development of numerous GNN variants, recent years have witnessed groundbreaking res…