极性不对称结构校准用于链接符号预测
Polarity-Asymmetric Structural Calibration for Link Sign Prediction
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中文总结 AI 辅助
提出极性不对称结构校准（PASC）框架，通过目标边结构先验与局部符号上下文比较得到冲突残差，校准注意力聚合、门控融合和优化，在五个真实符号网络数据集上取得最佳Macro-F1。
中文摘要 AI 辅助
链接符号预测（LSP）旨在推断符号网络中未观测链接的正或负极性。符号图神经网络（SGNNs）通常依赖符号图结构先验，包括结构平衡和类同质性相似性，来指导消息传递和预测。这些先验描述的是群体层面的倾向，而非对单个目标边的保证。在严重的符号不平衡下，它们的失败代价尤其高昂，此时少数类和局部冲突关系上的错误更难被检测和纠正。我们提出了极性不对称结构校准（PASC），一种用于符号链接预测的目标边结构先验校准框架。PASC构建一个仅基于结构的先验表示，估计目标边的结构先验分数，并将该分数与局部符号上下文线索进行比较，以得出冲突残差。该残差校准符号注意力聚合、目标边门控融合和机制自适应优化。在五个真实世界的符号网络数据集上的实验表明，PASC在代表性基线中始终取得最佳Macro-F1，同时具有竞争力的AUC、Binary-F1和Micro-F1。结构偏移实验进一步表明对密集邻域和局部闭合捷径的依赖减少。源代码可在https://this URL获取。
英文摘要
Link sign prediction (LSP) aims to infer the positive or negative polarity of unobserved links in signed networks. Signed Graph Neural Networks (SGNNs) usually rely on signed-graph structural priors, including structural balance and homophily-like similarity, to guide message passing and prediction. These priors describe population-level tendencies, not guarantees for individual target edges. Their failures are especially costly under severe sign imbalance, where errors on minority and locally conflicting relations are harder to detect and correct. We propose Polarity-Asymmetric Structural Calibration (PASC), a target-edge structural-prior calibration framework for signed link prediction. PASC constructs a structure-only prior representation, estimates a target-edge structural prior score, and compares this score with a local signed-context cue to derive a conflict residual. The residual calibrates signed attention aggregation, target-edge gated fusion, and regime-adaptive optimization. Experiments on five real-world signed network datasets show that PASC consistently achieves the best Macro-F1 among representative baselines, with competitive AUC, Binary-F1, and Micro-F1. Structural-shift experiments further suggest reduced dependence on dense-neighborhood and local-closure shortcuts. Source code is available at https://github.com/iqqGGGGGGG/PASC-for-LSP.
发表机构
- Northeast Normal University（东北师范大学）
- Jilin University（吉林大学）
机构由 AI 辅助整理，请以论文原文为准。
