Traditional network analysis focuses on binary edges, while real-world relationships are more nuanced, encompassing cooperation, neutrality, and conflict. The rise of negative edges in social media discussions spurred interest in analyzing signed interactions, especially in polarized debates. However, the vast data generated by digital networks presents challenges for traditional methods like Stochastic Block Models (SBM) and Exponential Family Random Graph Models (ERGM), particularly due to the homogeneity assumption and global dependence, which become increasingly unrealistic as network size grows. To address this, we propose a novel method that combines the strengths of SBM and ERGM while mitigating their weaknesses by incorporating local dependence based on nonoverlapping blocks. Our approach involves a two-step process: First, decomposing the network into sub-networks using SBM approximation, and, second, estimating parameters using ERGM methods. We validate our method on large synthetic networks and apply it to a signed Wikipedia network of thousands of editors. Through the use of local dependence, we find patterns consistent with structural balance theory.

翻译：传统网络分析主要关注二元边，而现实世界中的关系更为微妙，包含合作、中立与冲突。社交媒体讨论中负面边的兴起激发了对符号交互分析的兴趣，尤其是在极化辩论中。然而，数字网络产生的海量数据对传统方法如随机块模型（SBM）和指数族随机图模型（ERGM）提出了挑战，特别是同质性假设和全局依赖性在网络规模增大时变得日益不切实际。为解决这一问题，我们提出了一种新方法，该方法结合了SBM和ERGM的优势，同时通过引入基于非重叠块的局部依赖性来缓解其弱点。我们的方法包含两个步骤：首先，使用SBM近似将网络分解为子网络；其次，使用ERGM方法估计参数。我们在大型合成网络上验证了该方法，并将其应用于一个包含数千名编辑的符号维基百科网络。通过利用局部依赖性，我们发现了与结构平衡理论一致的模式。