9 citations · 11 across the 11 of their papers we have counts for
3 papers · 2 filters
A Graph Laplacian Eigenvector-based Pre-training Method for Graph Neural Networks
Howard Dai, Nyambura Njenga, Hiren Madhu +4
The development of self-supervised graph pre-training methods is a crucial ingredient in recent efforts to design robust graph foundation models (GFMs). Structure-based pre-trainin…
SlepNet: Spectral Subgraph Representation Learning for Neural Dynamics
Siddharth Viswanath, Rahul Singh, Yanlei Zhang +3
Graph neural networks have been useful in machine learning on graph-structured data, particularly for node classification and some types of graph classification tasks. However, the…
HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell Data
Siddharth Viswanath, Hiren Madhu, Dhananjay Bhaskar +7
In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Our wor…