3 papers
cs.LG2025
LoReTTA: A Low Resource Framework To Poison Continuous Time Dynamic Graphs
Himanshu Pal, Venkata Sai Pranav Bachina, Ankit Gangwal +1
Temporal Graph Neural Networks (TGNNs) are increasingly used in high-stakes domains, such as financial forecasting, recommendation systems, and fraud detection. However, their susc…
cs.LG2025
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs
Kishan Gurumurthy, Himanshu Pal, Charu Sharma
Graph Neural Network (GNN) research is rapidly advancing due to GNNs' capacity to learn distributed representations from graph-structured data. However, centralizing large volumes…
cs.LG2025
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning
Jayadratha Gayen, Himanshu Pal, Naresh Manwani +1
Many real-world systems can be modeled as dynamic graphs, where nodes and edges evolve over time, requiring specialized models to capture their evolving dynamics in risk-sensitive…