Decentralised Autonomous Organisations (DAO) can fragment when partisan communities emerge within their governance structures, leading to organisational splits known as "forks". We present a method to detect these emerging communities by analysing on-chain voting behaviour before fragmentation occurs. Our approach extracts voting events from governance smart contracts, constructs voter matrices encoding participation patterns, and applies pairwise dissimilarity analysis to quantify ideological divergence between addresses. We visualise these relationships using multidimensional scaling and identify partisan communities through k-means clustering with silhouette score optimisation. Using Nouns DAO as a case study, a protocol that has experienced multiple documented forks, we demonstrate that addresses destined to fork cluster together months before actual fragmentation events. Our analysis of 330 proposals spanning from contract deployment to the first major fork shows that 90% of fork addresses cluster together in the final 44 proposals, compared to only 47% in randomised data. These results indicate that partisan communities can be detected and visualised through on-chain governance analysis, offering early warnings of emerging divisions before they cause organisational fragmentation.

翻译：去中心化自治组织（DAO）在其治理结构中可能出现党派社群，导致组织分裂（即“分叉”）。本文提出一种方法，通过分析分叉发生前的链上投票行为来检测这些新兴社群。我们的方法从治理智能合约中提取投票事件，构建编码参与模式的投票者矩阵，并应用成对相异性分析量化地址之间的意识形态分歧。我们使用多维尺度分析可视化这些关系，并通过基于轮廓系数优化的k均值聚类识别党派社群。以多次经历有记载分叉的协议Nouns DAO为例，我们证明注定要分叉的地址在实际分叉事件发生前数月便已聚类。对从合约部署到首次重大分叉期间的330项提案进行分析表明，在最后44项提案中，90%的分叉地址聚集在一起，而随机数据中仅为47%。这些结果表明，通过链上治理分析可以检测并可视化党派社群，从而在组织分裂前提供新兴分歧的早期预警。