RECORD
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning
Machine Learning · Submitted 14 Jan 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The results highlight our model's ability to efficiently handle the trade-offs between prediction confidence and coverage, making it a dependable solution for applications requiring high precision in dynamic and uncertain environments.
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The abstract, sentence by sentence
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 applications effectively. Temporal graph neural networks (GNNs) are one such category of specialized models. 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 the uncertainty is high and confidence is low, thus minimizing the risk of critical misclassification and enhancing the results and reliability. We propose a coverage-based abstention prediction model to implement the reject option that maximizes prediction within a specified coverage. It improves the prediction score for link prediction and node classification tasks. Temporal GNNs deal with extremely skewed datasets for the next state prediction or node classification task. In the case of class imbalance, our method can be further tuned to provide a higher weightage to the minority class. Exhaustive experiments are presented on four datasets for dynamic link prediction and two datasets for dynamic node classification tasks. This demonstrates the effectiveness of our approach in improving the 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 the trade-offs between prediction confidence and coverage, making it a dependable solution for applications requiring high precision in dynamic and uncertain environments.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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 applications effectively. | background 1.00 | 0.05 | 0.03 | asserted 0.58 |
| s1 | Temporal graph neural networks (GNNs) are one such category of specialized models. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | For the first time, our approach integrates a reject option strategy within the framework of GNNs for continuous-time dynamic graphs. | contribution 0.98 | 0.93 | 0.09 | asserted 1.00 |
| s3 | This allows the model to strategically abstain from making predictions when the uncertainty is high and confidence is low, thus minimizing the risk of critical misclassification and enhancing the results and reliability. | method 0.49 | 0.51 | 0.26 | asserted 0.97 |
| s4 | We propose a coverage-based abstention prediction model to implement the reject option that maximizes prediction within a specified coverage. | method 0.76 | 0.92 | 0.11 | asserted 0.99 |
| s5 | It improves the prediction score for link prediction and node classification tasks. | result 0.98 | 0.84 | 0.19 | asserted 1.00 |
| s6 | Temporal GNNs deal with extremely skewed datasets for the next state prediction or node classification task. | background 0.98 | 0.13 | 0.03 | asserted 1.00 |
| s7 | In the case of class imbalance, our method can be further tuned to provide a higher weightage to the minority class. | method 0.75 | 0.46 | 0.03 | hedged possibility 0.79 |
| s8 | Exhaustive experiments are presented on four datasets for dynamic link prediction and two datasets for dynamic node classification tasks. | method 0.81 | 0.95 | 0.04 | asserted 0.96 |
| s9 | This demonstrates the effectiveness of our approach in improving the reliability and area under the curve (AUC)/ average precision (AP) scores for predictions in dynamic graph scenarios. | result 0.89 | 0.94 | 0.41 | asserted 1.00 |
| s10 | The results highlight our model's ability to efficiently handle the trade-offs between prediction confidence and coverage, making it a dependable solution for applications requiring high precision in dynamic and uncertain environments. | interpretation 0.98 | 0.84 | 0.68 | asserted 0.99 |
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The paper
Original abstract
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 applications effectively. Temporal graph neural networks (GNNs) are one such category of specialized models. 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 the uncertainty is high and confidence is low, thus minimizing the risk of critical misclassification and enhancing the results and reliability. We propose a coverage-based abstention prediction model to implement the reject option that maximizes prediction within a specified coverage. It improves the prediction score for link prediction and node classification tasks. Temporal GNNs deal with extremely skewed datasets for the next state prediction or node classification task. In the case of class imbalance, our method can be further tuned to provide a higher weightage to the minority class. Exhaustive experiments are presented on four datasets for dynamic link prediction and two datasets for dynamic node classification tasks. This demonstrates the effectiveness of our approach in improving the 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 the trade-offs between prediction confidence and coverage, making it a dependable solution for applications requiring high precision in dynamic and uncertain environments.
Human review
How the literature uses it
1 arXiv paper cite this in 2 sentences
Papers citing it
Cites it as background 1 paper
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Given a coverage, the model seeks to select a subset of examples that maximizes predictive performance while rejecting the remaining uncertain samples [ref] .
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification · Learning with Abstention · 0.91
What it cites
Cites as background 2 papers
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Few such models are memory-based TGN [ref] , MLP-based GraphMixer [ref] , graph transformer-based TCL [ref] , random-walk based CAWN [ref] , SSM-based DyGMamba [ref] etc.
DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models · Temporal Graph Encoder · 0.95
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Recent methods DyG-Mamba [ref] , DyGMamba [ref] are using node-level and time-level continuous state space model (SSM) to encode node interactions and dynamically select critical temporal information to improve robustness and generalization.
2408.06966 · Related Work · 0.98
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