让图嵌入自带不确定性度量，提升抗攻击能力。
REGE: A Method for Incorporating Uncertainty in Graph Embeddings
- 用课程学习融合数据噪声，用近似推断建模输出不确定性。
- 在对抗攻击下平均提升1.5%准确率，优于当前最优方法。
- 适合关注模型鲁棒性与可信度的图学习研究者。
现实应用中的图机器学习模型面临两大不确定性：一是数据不完整、含噪带来的不确定性，二是模型输出本身的不确定性，二者并非互斥。此外，模型易受定向对抗攻击，进一步放大上述不确定性。本文提出半径增强图嵌入（REGE），通过引入半径值表征模型输出的不确定性。REGE采用课程学习处理数据不确定性，利用近似推断（conformal learning）建模输出不确定性。实验表明，相较于现有最优方法，REGE在对抗攻击下平均提升1.5%的准确率。
原文摘要 · Abstract (English)
Machine learning models for graphs in real-world applications are prone to two primary types of uncertainty: (1) those that arise from incomplete and noisy data and (2) those that arise from uncertainty of the model in its output. These sources of uncertainty are not mutually exclusive. Additionally, models are susceptible to targeted adversarial attacks, which exacerbate both of these uncertainties. In this work, we introduce Radius Enhanced Graph Embeddings (REGE), an approach that measures and incorporates uncertainty in data to produce graph embeddings with radius values that represent the uncertainty of the model's output. REGE employs curriculum learning to incorporate data uncertainty and conformal learning to address the uncertainty in the model's output. In our experiments, we show that REGE's graph embeddings perform better under adversarial attacks by an average of 1.5% (accuracy) against state-of-the-art methods.
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