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This research examines the polycentric governance of digital assets inblockchain-based Decentralized Autonomous Organizations (DAOs). It offers atheoretical framework and addresses a critical challenge facing decentralizedgovernance by developing a method to identify sybils, or spurious identities.The method uses graph deep learning techniques to identify sybil activity in aDAO governance dataset (snapshot.org). Specifically, a Graph ConvolutionalNeural Network (GCNN) learned voting behaviours and a fast k-means vectorclustering algorithm (FAISS) used the high dimensional embeddings to identifysimilar nodes in a graph. The results reveal that deep learning can effectivelyidentify sybils, reducing the voting graph by 2-5%. This research underscoresthe importance of sybil resistance in DAOs and offers a novel perspective ondecentralized governance, informing future policy, regulation, and governancepractices.

New Online Communities: Graph Deep Learning on Anonymous Voting Networks to Identify Sybils in Polycentric Governance