面向嵌入的极化管理在有向网络中
Embedding-aware Polarization Management in Signed Networks
AI总结：
EPM提出了一种统一的极化管理框架，通过有效电阻和结构平衡中介节点缓解极化，保持任务相关网络结构。
摘要：
在有向网络中，嵌入式网络嵌入（SNE）被广泛用于表示具有正负关系的网络，但其在下游分析流程中的重复使用可能会无意中加强结构极化。现有的极化度量主要是为无向网络设计的，或依赖于预定义的观点状态，限制了它们在基于嵌入分析的有向网络中的应用。我们提出EPM，一种统一的极化管理框架，该框架在嵌入空间中联合测量和减轻极化。EPM引入了一种基于嵌入的极化度量，该度量基于有效电阻，并通过结构平衡的中介节点进行局部增强来实现结构感知的缓解策略。在真实世界有向网络上的实验表明，EPM在保持任务相关网络结构的同时有效减轻了极化。EPM的代码库可在https://github.com/JeonghanSon/EPM-Embedding-aware-Polarization-Management上获得。
英文摘要：
Signed network embeddings (SNE) are widely used to represent networks with positive and negative relations, but their repeated use in downstream analysis pipelines can inadvertently reinforce structural polarization. Existing polarization measures are largely designed for unsigned networks or rely on predefined opinion states, limiting their applicability to embedding-based analysis in signed settings. We propose EPM, a unified polarization management framework that jointly measures and mitigates polarization in the embedding space. EPM introduces an embedding-based polarization measure grounded in effective resistance and a structure-aware mitigation strategy via localized augmentation through structurally balanced intermediary nodes. Experiments on real-world signed networks demonstrate that EPM effectively mitigates polarization while preserving task-relevant network structure. The codebase of EPM is available at https://github.com/JeonghanSon/EPM-Embedding-aware-Polarization-Management.
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。
