Concentration in Governance Control Across Decentralised Finance Protocols

TL;DR

The paper investigates cross-protocol governance in DeFi by analyzing shared governance-token holdings across multiple protocols using Statistically Validated Networks (SVNs). It constructs a bipartite token-address network, applies a hypergeometric null model with Bonferroni correction, and defines link-defining addresses that hold pairs of tokens, enabling detailed metrics on internal and directional influence. Key findings show persistent, small link-defining address sets dominated by institutional actors who hold substantial token wealth and exert disproportionate influence within token communities, with influence patterns shifting during market cycles. These insights reveal potential cross-protocol governance risk, where a few entities could influence multiple protocols, particularly during speculative periods, underscoring the need for awareness of cross-protocol concentration in governance design and future work to refine label accuracy and token coverage.

Abstract

Blockchain-based systems are frequently governed through tokens that grant their holders voting rights over core protocol functions and funds. The centralisation occurring in Decentralised Finance (DeFi) protocols' token-based voting systems is typically analysed by examining token holdings' distribution across addresses. In this paper, we expand this perspective by exploring shared token holdings of addresses across multiple DeFi protocols. We construct a Statistically Validated Network (SVN) based on shared governance token holdings among addresses. Using the links within the SVN, we identify influential addresses that shape these connections and we conduct a post-hoc analysis to examine their characteristics and behaviour. Our findings reveal persistent influential links over time, predominantly involving addresses associated with institutional investors who maintain significant token supplies across the sampled protocols. Finally, we observe that token holding patterns and concentrations tend to shift in response to speculative market cycles.

Figures (12)

• Figure 1: Jaccard similarity of the SVNs over time.
• Figure 2: Distribution of the number of addresses across the sampling period that define validated links between governance tokens in validated token projections.
• Figure 3: Influence exerted by addresses within token links.
• Figure 4: A collapsed time view of internal influence metrics across token links, filtered for links that occurred at least nine times.
• Figure 5: Gini coefficient showing the inequality of influence within token links occurring nine or more times.