1 citations · 3 across the 10 of their papers we have counts for
10 papers
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…
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…
Commutative algebra-enhanced topological data analysis
Chuanshen Hu, Yu Wang, Kelin Xia +2
Topological Data Analysis (TDA) combines computational topology and data science to extract and analyze intrinsic topological and geometric structures in data set in a metric space…
TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment
Bingqing Han, Yipeng Zhang, Longlong Li +2
Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with mon…
Molecular topological deep learning for polymer property prediction
Cong Shen, Yipeng Zhang, Fei Han +1
Accurate and efficient prediction of polymer properties is of key importance for polymer design. Traditional experimental tools and density function theory (DFT)-based simulations…
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…