Jayadratha Gayen supervised by Dr. Charu Sharma  received his Master of Science  in Electronics and Communication Engineering (ECE). Here’s a summary of his research work on Abstaining to Predict Right: Reliable Graph Neural Networks through Strategic Rejection:

Many real-world systems can be modeled as dynamic graphs, where nodes and edges evolve with time. Graph Neural Networks (GNNs) excel at modeling relational data. Temporal GNNs capture the dynamics of time-changing data very well. Still, their reliability in risk-sensitive domains where errors in fraud detection, legal judgments, or medical diagnoses carry severe consequences remains limited. Traditional GNNs lack mechanisms to quantify uncertainty or abstain from low-confidence predictions, particularly in dynamic systems where temporal evolution and class imbalance amplify ambiguity. This thesis addresses these gaps by introducing strategic abstention mechanisms into graph learning, helping models to prioritize high-confidence decisions while rejecting uncertain predictions. We unify this approach across dynamic and static graphs, advancing reliability in high-stakes applications through uncertainty-aware frameworks.

 For the first time, our approach integrates a reject option strategy within the framework of GNNs for continuous-time dynamic graphs. This allows the model to strategically abstain from making predictions when uncertainty is high and confidence is low, minimizing the risk of critical misclassification and enhancing reliability. We propose a coverage-based abstention prediction model to implement the reject option that maximizes predictions within specified coverage. It improves prediction scores for link prediction and node classification tasks. Temporal GNNs deal with skewed datasets for the next state prediction or node classification. In cases of class imbalance, our method can be tuned to provide a higher weight to the minority class. Exhaustive experiments are presented on four datasets for dynamic link prediction and two for dynamic node classification tasks. This demonstrates our approach’s effectiveness in improving reliability and area under the curve (AUC)/average precision (AP) scores for predictions in dynamic graph scenarios. The results highlight our model’s ability to efficiently handle trade-offs between prediction confidence and coverage, making it a dependable solution for applications requiring high precision in dynamic and uncertain environments.

 Beyond temporal graphs, we extend the concept of classification with the reject option to static graph settings. We reformulate legal judgment prediction (LJP) as node classification on citation networks (ILDCdataset), integrating cost- and coverage-based abstention. Our models (NCwR-Cost/NCwR-Cov) improve accuracy by rejecting uncertain cases, ensuring reliability in legal decision-making. SHAP based explanations reveal case-specific abstention rationale, enhancing transparency. Further validation on medical datasets (thyroid diagnosis, diabetes prediction) confirms cross-domain validation, with abstention mechanisms reducing misclassification risks in ambiguous cases. This work provides deployable solutions for applications where reliability is non-negotiable.

July 2025