This research examines the polycentric governance of digital assets in Decentralized Autonomous Organizations (DAOs). It offers a theoretical framework and addresses a critical challenge facing decentralized governance by developing a method to identify sybils, or spurious identities. The method uses graph deep learning techniques to identify sybil activity in a DAO governance dataset (snapshot.org). Specifically, a Graph Convolutional Neural Network (GCNN) learned voting behaviours and a fast k-means vector clustering algorithm (FAISS) used the high dimensional embeddings to identify similar nodes in a graph. The results reveal that deep learning can effectively identify sybils, reducing the voting graph by 2-5%. This research underscores the importance of sybil resistance in DAOs and offers a novel perspective on decentralized governance, informing future policy, regulation, and governance practices.

该研究探讨了去中心化自治组织（DAO）中数字资产的多中心治理，并提供了一个理论框架，通过开发一种识别虚假身份（Sybils）的方法，解决了去中心化治理面临的关键挑战。该方法使用图深度学习技术在一个DAO治理数据集（snapshot.org）中识别虚假活动。具体而言，图卷积神经网络（GCNN）学习了投票行为，快速k均值向量聚类算法（FAISS）利用高维嵌入识别图中的相似节点。结果表明，深度学习可以有效减少2-5%的投票图中的虚假节点。该研究强调了去中心化自治组织中防止虚假身份的重要性，并为去中心化治理提供了一种新的视角，为未来的政策、监管和治理实践提供了指导。

匿名投票网络上的图深度学习用于在多中心治理中识别Sybil