資產部署於公鏈不代表市場結構自然分散；白名單、投資人資格、託管與申購贖回仍可能使代幣移轉依賴少數功能性中介。本研究選取 BUIDL、BENJI、OUSG 與 USDY，建立 14 個產品鏈別子網絡，透過比較性社會網絡分析，檢視地址持有集中、代幣移轉網絡中介依賴及節點移除下的結構敏感性。四項產品皆與美國政府證券或短期美元收益資產相關，但法律與金融形式不同，故本文將其視為同一研究場域中的異質案例，而非單一類型的美國國債代幣。

研究依「制度與鏈別情境—鏈上參與及移轉—持有與代幣移轉網絡—結構敏感性」推進。H1 以期末正餘額及 Gini、HHI、Top-1、Nakamoto 係數與熵值衡量地址持有分布；H2 由清理後有效地址間移轉建立有向網絡，結合中心性、核心層、社群與地址角色查核辨識功能性中介；H3 直接沿用 H2 網絡，比較介數、度數及節點強度目標式移除與隨機基準的 LCC／LCCR、FER、LFRR 與完整曲線。14 個子網絡用於樣本及可判讀的 H1 分析，其中六個具至少 50 個清理後節點與 50 條有效邊者納入 H2／H3 深度比較。所有可分析子網絡的隨機移除均統一為每一比例 100 次；BENJI–Stellar 另排除非帳戶型餘額物件與發行帳戶供給操作。

結果顯示，各產品鏈別呈現不同程度的地址分布不均與頭部集中。BUIDL 的移轉網絡偏向機構申贖相關核心；BENJI–Stellar 集中於少數拓樸橋接位置，但未驗證地址不作實體歸因；OUSG–Ethereum 呈現管理與包裝層中介；USDY 則由跨鏈橋、交易、流動性及管理設施共同連接。H3 整體獲得方向性支持：目標式移除通常較隨機基準造成更大結構變化，但不同排序與 LCC、FER、LFRR 的訊號並不完全一致。由於網絡規模、邊數與觀測期間不同，本文不作跨網絡精確風險排名，微型網絡亦只作描述。

本文據此提出「中介形式的轉化」：代幣化未必消除金融中介，而可能將發行、申贖、託管、管理、橋接與流動性功能轉化為鏈上可觀察的地址、合約與市場設施。中介類型增加或網絡規模擴大，皆不保證替代路徑充分。本文結果限於地址層級歷史結構，不構成制度因果效果、實體層級集中或金融壓力測試。

Public-blockchain deployment does not inherently decentralize market structure: whitelisting, investor eligibility, custody, and subscription-redemption arrangements may still concentrate token transfers around a limited set of functional intermediaries. This study uses comparative social network analysis to examine address-level holding concentration, token-transfer intermediation, and structural sensitivity under node removal across 14 subnetworks of BUIDL, BENJI, OUSG, and USDY. These legally and financially distinct products are treated as heterogeneous cases of tokenized U.S. government securities and short-term USD yield-bearing real-world assets rather than as a homogeneous class of Treasury tokens.

The analysis follows an institutional-and-chain context—on-chain participation and transfer—network structure—structural sensitivity sequence. H1 measures end-period address distributions using Gini, HHI, top-1 share, Nakamoto coefficients, and entropy. H2 constructs directed networks from cleaned address-to-address transfers and evaluates centrality, cores, communities, and verified address roles. H3 compares betweenness-, degree-, and node-strength-based targeted removal with 100-run random baselines using LCC/LCCR, historical flow exposure (FER), flow retention in the largest component (LFRR), and full removal curves. All 14 subnetworks support sample description and interpretable H1 analysis; six meeting the threshold of at least 50 cleaned nodes and 50 cleaned edges receive in-depth H2/H3 analysis. BENJI–Stellar additionally excludes non-account balance objects and issuer supply operations.

The results reveal varying degrees of address inequality and top-holder concentration. BUIDL exhibits an institution-oriented subscription-redemption core; BENJI–Stellar depends on a small number of topological bridges without assigning unverified addresses to institutional entities; OUSG–Ethereum reflects management- and wrapper-layer intermediation; and USDY is connected through bridging, trading, liquidity, and management facilities. H3 receives directional support: targeted removal generally causes greater structural change than random removal, although signals vary across rankings and metrics. The study therefore advances an intermediation-transformation perspective: tokenization may render issuance, redemption, custody, management, bridging, and liquidity functions observable on-chain rather than eliminate them. The findings describe historical address-level structure and do not establish institutional causality, entity-level concentration, precise cross-network risk rankings, or financial stress-test results.

Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564–608. https://doi.org/10.1257/aer.20130456
Aggarwal, S., Balakrishnan, N., Wang, A., & Li, J. (2025). Analyzing tokenization of U.S. Treasuries as on-chain real-world assets: Product structure, market implications, and regulatory concerns. Journal of Student-Scientists’ Research. https://doi.org/10.13021/jssr2025.5255
Agur, I., Villegas-Bauer, G., Mancini-Griffoli, T., Martinez Peria, M. S., & Tan, B. (2025). Tokenization and financial market inefficiencies (FinTech Note No. 2025/001). International Monetary Fund. https://www.imf.org/en/publications/fintech-notes/issues/2025/01/29/tokenization-and-financial-market-inefficiencies-561256
Albert, R., Jeong, H., & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378–382. https://doi.org/10.1038/35019019
Aldasoro, I., Doerr, S., Gambacorta, L., Garratt, R., & Wilkens, P. K. (2023). The tokenisation continuum. BIS Bulletin, (72). https://www.bis.org/publ/bisbull72.pdf
Allen, F., & Gale, D. (2000). Financial contagion. Journal of Political Economy, 108(1), 1–33. https://doi.org/10.1086/262109
Ao, Z., Cong, L. W., Horvath, G., & Zhang, L. (2022). Is decentralized finance actually decentralized? A social network analysis of the Aave protocol on the Ethereum blockchain. arXiv preprint. https://arxiv.org/abs/2206.08401
Ba, S., Wang, Y., Bai, H., & Zhu, H. (2025). RWA tokenisation in Web 3.0 era. Springer. https://doi.org/10.1007/978-981-96-7663-7
Baltais, M., Sondore, E., Putniņš, T. J., & Karlsen, J. R. (2024). Economic impact potential of real-world asset tokenization [UTS Business School, University of Technology Sydney, Report 2024–06]. https://www.macroeconomics.lv/sites/default/files/2024-06/06_Baltais%20%26%20Sondore_0.pdf
Bank for International Settlements. (2025). III. The next-generation monetary and financial system (Annual Economic Report). https://www.bis.org/publ/arpdf/ar2025e3.htm
Battiston, S., Puliga, M., Kaushik, R., Tasca, P., & Caldarelli, G. (2012). DebtRank: Too central to fail? Financial networks, the FED and systemic risk. Scientific Reports, 2, 541. https://doi.org/10.1038/srep00541
BlackRock. (2024, March 20). BlackRock launches its first tokenized fund, BUIDL, on the Ethereum network. Retrieved July 13, 2026, from https://www.blackrock.com/corporate/newsroom/press-releases/article/corporate-one/press-releases/blackrock-launches-first-tokenized-fund-buidl-on-the-ethereum-network
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Borgatti, S. P., & Everett, M. G. (1999). Models of core/periphery structures. Social Networks, 21(4), 375–395. https://doi.org/10.1016/S0378-8733(99)00019-2
Born, A., Gati, Z., Lambert, C., Naeem, M., & Pellicani, A. (2026). Who to regulate? Identifying actors within DeFi’s governance (Working Paper Series No. 3208) (Working paper version). European Central Bank. Retrieved June 25, 2026, from https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp3208~051a880042.en.pdf
Brunnermeier, M. K., & Pedersen, L. H. (2009). Market liquidity and funding liquidity. The Review of Financial Studies, 22(6), 2201–2238. https://doi.org/10.1093/rfs/hhn098
Caccioli, F., Shrestha, M., Moore, C., & Farmer, J. D. (2014). Stability analysis of financial contagion due to overlapping portfolios. Journal of Banking & Finance, 46, 233–245. https://doi.org/10.1016/j.jbankfin.2014.05.021
Cont, R., Moussa, A., & Santos, E. B. e. (2013). Network structure and systemic risk in banking systems. In J.-P. Fouque & J. A. Langsam (Eds.), Handbook on systemic risk (pp. 327–368). Cambridge University Press. https://doi.org/10.1017/CBO9781139151184.018
De Collibus, F. M., Campajola, C., Caldarelli, G., & Tessone, C. J. (2024). Patterns and centralisation in Ethereum-based token transaction networks. Frontiers in Physics, 12, 1305167. https://doi.org/10.3389/fphy.2024.1305167
Franklin Templeton. (2026). Franklin OnChain U.S. Government Money Fund (FOBXX). Retrieved July 13, 2026, from https://www.franklintempleton.com/investments/options/money-market-funds/products/29386/SINGLCLASS/franklin-onchain-u-s-government-money-fund/FOBXX
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. https://doi.org/10.2307/3033543
Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. https://doi.org/10.1016/0378-8733(78)90021-7
Glasserman, P., & Young, H. P. (2016). Contagion in financial networks. Journal of Economic Literature, 54(3), 779–831. https://doi.org/10.1257/jel.20151228
Haldane, A. G., & May, R. M. (2011). Systemic risk in banking ecosystems. Nature, 469(7330), 351–355. https://doi.org/10.1038/nature09659
International Organization of Securities Commissions. (2025). Tokenization of financial assets (Final Report No. FR/17/2025). https://www.iosco.org/library/pubdocs/pdf/IOSCOPD809.pdf
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. https://doi.org/10.1093/comnet/cnu016
Kondor, D., Pósfai, M., Csabai, I., & Vattay, G. (2014). Do the rich get richer? An empirical analysis of the Bitcoin transaction network. PLOS ONE, 9(2), e86197. https://doi.org/10.1371/journal.pone.0086197
Latapy, M., Magnien, C., & Del Vecchio, N. (2008). Basic notions for the analysis of large two-mode networks. Social Networks, 30(1), 31–48. https://doi.org/10.1016/j.socnet.2007.04.006
Li, J. (2025). Current landscape of the real-world asset (RWA) tokenization ecosystem. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.6077226
Luo, J., Tinn, K., Duran, S. F., Wu, D., & Liu, X. (2025). Transaction profiling and address role inference in tokenized U.S. Treasuries. arXiv preprint. https://arxiv.org/abs/2507.14808
Newman, M. E. J. (2010). Networks: An introduction. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199206650.001.0001
Ondo Finance. (2026a). OUSG token: Overview. Retrieved July 13, 2026, from https://docs.ondo.finance/qualified-access-products/ousg/overview
Ondo Finance. (2026b). USDY basics. Retrieved July 13, 2026, from https://docs.ondo.finance/general-access-products/usdy/basics
Poledna, S., Molina-Borboa, J. L., Martínez-Jaramillo, S., van der Leij, M., & Thurner, S. (2015). The multi-layer network nature of systemic risk and its implications for the costs of financial crises. Journal of Financial Stability, 20, 70–81. https://doi.org/10.1016/j.jfs.2015.08.001
Securitize. (2024, March 20). BlackRock launches its first tokenized fund, BUIDL, on the Ethereum network. Retrieved July 13, 2026, from https://securitize.io/learn/press/blackrock-launches-first-tokenized-fund-buidl-on-the-ethereum-network
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Srinivasan, B. S., & Lee, L. (2017). Quantifying decentralization. https://news.earn.com/quantifying-decentralization-e39db233c28e
Tao, B., Dai, H.-N., Wu, J., Ho, I. W.-H., Zheng, Z., & Cheang, C. F. (2022). Complex network analysis of the Bitcoin transaction network. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(3), 1009–1013. https://doi.org/10.1109/TCSII.2021.3127952
U.S. Department of Justice & Federal Trade Commission. (2010). Horizontal merger guidelines. https://www.justice.gov/atr/horizontal-merger-guidelines-08192010
Watsky, C., Liu, M., Ly, N., Orr, K., Seira, A., Vida, Z., & Wu, L. (2024). Tokenized assets on public blockchains: How transparent is the blockchain? FEDS Notes. https://doi.org/10.17016/2380-7172.3444
Yan, T., & Tessone, C. J. (2025). Network analysis of Uniswap: Centralization and fragility in the decentralized exchange market. Journal of the Physical Society of Japan Conference Proceedings, 44, 011013. https://doi.org/10.7566/JPSCP.44.011013