3 papers
Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
Ayushman Raghuvanshi, Thummaluru Siddartha Reddy, Sundeep Prabhakar Chepuri +1
Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data. Long-range information propagation is a key…
Task-driven Heterophilic Graph Structure Learning
Ayushman Raghuvanshi, Gonzalo Mateos, Sundeep Prabhakar Chepuri
Graph neural networks (GNNs) often struggle to learn discriminative node representations for heterophilic graphs, where connected nodes tend to have dissimilar labels and feature s…
Covariance Scattering Transforms
Andrea Cavallo, Ayushman Raghuvanshi, Sundeep Prabhakar Chepuri +1
Machine learning and data processing techniques relying on covariance information are widespread as they identify meaningful patterns in unsupervised and unlabeled settings. As a p…