Testing for Balance in Social Networks
社会网络平衡性检验AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接检验社会网络中正负关系与三角形结构，属于网络分析。
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社会网络平衡性检验
- 研究问题
- 在友好与敌对关系并存的社会网络中，如何检验含奇数条负边的三角形是否稀少，同时考虑正负关系的差异？
- 方法与思路
- 作者提出考虑正负边差异的平衡性检验，针对原零模型推导统计量的渐近正态性，并分析洪都拉斯村庄的有符号网络。
- 材料陈述的贡献
- 摘要称，新检验比边符号置换方法更准确；在所收集的三十二个村庄网络中，仅发现关系形成符合平衡理论的微弱证据。
- 阅读关注点
- 应核对零模型如何描述正负边、统计量如何构造及渐近条件，并检查网络采集、缺失关系和检验功效的影响。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，零模型和准确性比较细节未说明；村庄结果不能推广到所有社会网络，不能推断刊本变化。
原文摘要
Friendship and antipathy exist in concert with one another in real social networks. Despite the role they play in social interactions, antagonistic ties are poorly understood and infrequently measured. One important theory of negative ties that has received relatively little empirical evaluation is balance theory, the codification of the adage “the enemy of my enemy is my friend” and similar sayings. Unbalanced triangles are those with an odd number of negative ties, and the theory posits that such triangles are rare. To test for balance, previous works have used a permutation test on the edge signs. The flaw in this method, however, is that it assumes that negative and positive edges are interchangeable. In reality, they could not be more different. Here, we propose a novel test of balance that accounts for this discrepancy and show that our test is more accurate at detecting balance. Along the way, we prove asymptotic normality of the test statistic under our null model, which is of independent interest. Our case study is a novel dataset of signed networks we collected from 32 isolated, rural villages in Honduras. Contrary to previous results, we find that there is only marginal evidence for balance in social tie formation in this setting.