通过结构不变性实现跨领域知识迁移，提升噪声下的模型稳定性。
Importance inversion transfer identifies shared principles for cross-domain learning
- 提出重要性反转迁移机制，聚焦跨域共性结构特征。
- 在极端噪声下决策稳定性提升56%，优于传统方法。
- 适合需要跨学科知识迁移的研究者参考。
跨科学领域的知识迁移依赖于共享的组织原则。然而，现有迁移学习方法在面对根本异构系统时往往失效，尤其在数据极度稀缺或存在随机噪声的情况下。本研究提出可解释跨域迁移学习（X-CDTL）框架，融合网络科学与可解释人工智能，识别生物、语言、分子及社会网络中的结构不变性。通过引入重要性反转迁移（IIT）机制，该框架优先关注域间不变的结构锚点，而非特定域独有的判别性特征。在异常检测任务中，受此原则指导的模型表现出显著性能提升——在极端噪声下决策稳定性相对提升56%，超过传统基线。结果表明，异构领域间存在共享的组织特征，为跨学科知识传播建立了原理性范式。通过从隐式表征转向显式结构规律，本工作推动机器学习成为稳健的科学发现引擎。
原文摘要 · Abstract (English)
The capacity to transfer knowledge across scientific domains relies on shared organizational principles. However, existing transfer-learning methodologies often fail to bridge radically heterogeneous systems, particularly under severe data scarcity or stochastic noise. This study formalizes Explainable Cross-Domain Transfer Learning (X-CDTL), a framework unifying network science and explainable artificial intelligence to identify structural invariants that generalize across biological, linguistic, molecular, and social networks. By introducing the Importance Inversion Transfer (IIT) mechanism, the framework prioritizes domain-invariant structural anchors over idiosyncratic, highly discriminative features. In anomaly detection tasks, models guided by these principles achieve significant performance gains - exhibiting a 56% relative improvement in decision stability under extreme noise - over traditional baselines. These results provide evidence for a shared organizational signature across heterogeneous domains, establishing a principled paradigm for cross-disciplinary knowledge propagation. By shifting from opaque latent representations to explicit structural laws, this work advances machine learning as a robust engine for scientific discovery.
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