6 citations · 6 across the 14 of their papers we have counts for
12 papers · 1 filter
Path Laplacian Encodings for Directed Graphs
Lydia Mezrag, Semih Cantürk, Michael Perlmutter +2
Directed graphs naturally model many real-world systems in which interactions are asymmetric, such as citation networks, web graphs, and information-flow networks. However, graph l…
BrainDyn: A Sheaf Neural ODE for Forecasting and Generating Brain Dynamics
Siddharth Viswanath, Panayiotis Ketonis, Chen Liu +11
Brain activity unfolds as coordinated patterns across regions over time, and modeling these dynamics is a central goal in computational neuroscience. Because activity is distribute…
Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
Semih Cantürk, Thomas Sabourin, Frederik Wenkel +2
A key challenge in developing unified neural solvers for combinatorial optimization (CO) is the efficient generalization of models from a given set of tasks to new tasks unseen dur…
VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks
David R. Johnson, Alexander Sietsema, Rishabh Anand +3
We introduce vector diffusion wavelets (VDWs), a novel family of wavelets inspired by the vector diffusion maps algorithm that was introduced to analyze data lying in the tangent b…
InfoGain Wavelets: Furthering the Design of Graph Diffusion Wavelets
David R. Johnson, Smita Krishnaswamy, Michael Perlmutter
Diffusion wavelets extract information from graph signals at different scales of resolution by utilizing graph diffusion operators raised to various powers, known as diffusion scal…
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…
ProtScape: A molecular structure and energy-aware representation for protein conformation generation
Siddharth Viswanath, Dhananjay Bhaskar, Xingzhi Sun +13
Molecular dynamics (MD) simulations are a principled but computationally expensive approach for studying protein conformational variability, making it challenging to generate large…
Convergence of Manifold Filter-Combine Networks
David R. Johnson, Joyce Chew, Siddharth Viswanath +4
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). The filter-combine framework parallels the popular aggregate-c…
Bayesian Formulations for Graph Spectral Denoising
Sam Leone, Xingzhi Sun, Michael Perlmutter +1
Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as…
BLIS-Net: Classifying and Analyzing Signals on Graphs
Charles Xu, Laney Goldman, Valentina Guo +7
Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. However, much less work has been done on signal classif…
Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis
Aarthi Venkat, Joyce Chew, Ferran Cardoso Rodriguez +3
Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular s…
DYMAG: Rethinking Message Passing Using Dynamical-systems-based Waveforms
Dhananjay Bhaskar, Xingzhi Sun, Yanlei Zhang +8
We present DYMAG, a graph neural network based on a novel form of message aggregation. Standard message-passing neural networks, which often aggregate local neighbors via mean-aggr…