1 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
Recovering topological information of light by topological learning
Benquan Wang, Trishita Das, Yuhan Peng +8
The evolution of modern-day communication networks towards enhanced capacity and robustness is driving interest in topological light wave as a next-generation information carrier.…
Demystifying Oversmoothing in Sheaf Neural Networks: An Index-Theoretic Criterion
Junwen Dong, Yuhan Peng, Hao Li +2
To combat oversmoothing in Graph Convolutional Networks, Sheaf Neural Networks (SNNs) were proposed as a generalization by equipping the graph with a sheaf structure and replacing…
Communicability-Inspired Positional Encoding (CIPE)
Yipeng Zhang, Zhongtian Sun, Pietro Liò +1
Positional encodings (PEs) are essential for Transformers. Yet designing effective PEs for non-Euclidean graphs remains challenging. Such encodings should ideally induce an Attenti…
Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
Yuhan Peng, Junwen Dong, Yuzhi Zeng +6
Graph neural networks face two fundamental challenges rooted in the linear structure of Euclidean vector spaces: (1) Current architectures represent geometry through vectors (direc…
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…