4 papers · 1 filter
Kernelized Edge Attention: Addressing Semantic Attention Blurring in Temporal Graph Neural Networks
Govind Waghmare, Srini Rohan Gujulla Leel, Nikhil Tumbde +3
Temporal Graph Neural Networks (TGNNs) aim to capture the evolving structure and timing of interactions in dynamic graphs. Although many models incorporate time through encodings o…
Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs
Arman Gupta, Govind Waghmare, Gaurav Oberoi +1
In heterophilic graphs, where neighboring nodes often belong to different classes, conventional Graph Neural Networks (GNNs) struggle due to their reliance on local homophilous nei…
A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation
Vennela Yarabolu, Govind Waghmare, Sonia Gupta +1
In many real-world applications, continuous machine learning (ML) systems are crucial but prone to data drift, a phenomenon where discrepancies between historical training data and…
Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes
Govind Waghmare, Ankur Debnath, Siddhartha Asthana +1
Temporal Point Processes (TPP) are probabilistic generative frameworks. They model discrete event sequences localized in continuous time. Generally, real-life events reveal descrip…