4 citations · 6 across the 3 of their papers we have counts for
3 papers
A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks
Sai Munikoti, Deepesh Agarwal, Laya Das +1
Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data. We consider the p…
Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks
Sai Munikoti, Laya Das, Balasubramaniam Natarajan
In the quest to improve efficiency, interdependence and complexity are becoming defining characteristics of modern complex networks representing engineered and natural systems. Gra…
Scalable Graph Neural Network-based framework for identifying critical nodes and links in Complex Networks
Sai Munikoti, Laya Das, Balasubramaniam Natarajan
Identifying critical nodes and links in graphs is a crucial task. These nodes/links typically represent critical elements/communication links that play a key role in a system's per…