标题： 局部依赖下的可扩展有符号指数随机图模型 显示英文标题

标题： Scalable Signed Exponential Random Graph Models under Local Dependence

摘要： 传统网络分析关注二元边，而现实世界的关系更加复杂，包括合作、中立和冲突。 社交媒体讨论中负边的兴起引发了对有符号交互分析的兴趣，特别是在两极分化的辩论中。 然而，数字网络产生的大量数据给传统方法如随机块模型（SBM）和指数族随机图模型（ERGM）带来了挑战，特别是由于同质性假设和全局依赖性，随着网络规模的增长，这些假设变得越来越不现实。 为了解决这个问题，我们提出了一种新方法，结合SBM和ERGM的优势，同时通过基于非重叠块的局部依赖性来缓解它们的弱点。 我们的方法包括一个两步过程：首先使用SBM近似将网络分解为子网络，然后使用ERGM方法估计参数。 我们在大型合成网络上验证了我们的方法，并将其应用于一个包含数千名编辑的有符号维基百科网络。 通过使用局部依赖性，我们发现了与结构平衡理论一致的模式。 显示英文摘要

摘要： 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 non-overlapping blocks. Our approach involves a two-step process: first, decomposing the network into sub-networks using SBM approximation, and then 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.