3 papers
cs.LG2026
'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning
Shubhajit Roy, Anirban Dasgupta
Dynamic graph neural networks (DGNNs) that operate on snapshot sequences typically fall into one of two categories. \emph{Temporal-first} approaches build per-node temporal embeddi…
cs.LG2026
FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening
Shubhajit Roy, Hrriday Ruparel, Kishan Ved +1
Scalability of Graph Neural Networks (GNNs) remains a significant challenge. To tackle this, methods like coarsening, condensation, and computation trees are used to train on a sma…
cs.LG2025
Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networks
Shubhajit Roy, Shrutimoy Das, Binita Maity +2
Subgraph counting is a fundamental task for analyzing structural patterns in graph-structured data, with important applications in domains such as computational biology and social…