自信预测，准确预测：动态图学习中的弃权机制
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning
AI总结：
本研究首次将拒绝选项策略整合进连续时间动态图GNN框架，提出基于覆盖率的弃权预测模型，可在高不确定时弃权以降低误分类风险，还可适配类别不平衡场景，在多数据集上验证了其提升预测可靠性与AUC/AP得分的效果。
摘要：
许多真实世界系统可建模为动态图，其节点和边随时间演化，需要专门的模型来有效捕捉风险敏感应用中的演化动态。时序图神经网络（GNNs）正是这类专门模型的一个类别。我们的方法首次将拒绝选项策略整合进连续时间动态图的GNN框架中，这使得模型在不确定性高、置信度低时能够策略性地弃权（不执行）预测，从而将严重误分类的风险降至最低，提升预测结果与可靠性。我们提出了一种基于覆盖率的弃权预测模型来实现该拒绝选项，可在指定覆盖率内最大化预测效果，提升了链路预测和节点分类任务的预测得分。时序GNN在处理下一状态预测或节点分类任务时，会面对极度不平衡的数据集。在类别不平衡的场景下，我们的方法可进一步调整，为少数类赋予更高权重。我们在4个动态链路预测数据集和2个动态节点分类数据集上开展了详尽实验，结果证明了我们的方法在提升动态图场景下预测的可靠性以及曲线下面积（AUC）/平均精度（AP）得分方面的有效性。结果凸显了我们的模型能够高效处理预测置信度与覆盖率之间的权衡，是动态且不确定环境中对精度要求高的应用的可靠解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
