A Full-History Network Dataset for BTC Asset Decentralization Profiling

TL;DR

This paper presents the first systematic investigation to profile BTC’s asset decentralization and design several decentralization degrees for quantification, and emphasizes the significant role of network properties and the network-based decentralization degree in enhancing Bitcoin analysis.

Abstract

Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective. In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC's asset decentralization and design several decentralization degrees for quantification. Through extensive experiments, we emphasize the significant role of network properties and our network-based decentralization degree in enhancing Bitcoin analysis. Our findings demonstrate the importance of our comprehensive dataset and analysis in advancing research on Bitcoin's transaction dynamics and decentralization, providing valuable insights into the network's structure and its implications.

Figures (7)

• Figure 1: Evolution of centrality and dispersion rate of four key centrality metrics: (a) Betweenness, (b) Closeness, (c) In-Degree, and (d) PageRank. These metrics are plotted for different percentiles of nodes. For dispersion rate, lower dispersion values suggest a more decentralized network structure.
• Figure 2: The left plot illustrates the proportion of total Bitcoins held by the top-$x$ addresses over time, with $x$ ranging from 500 to 5000 in increments of 500. The middle plot shows the proportional difference in Bitcoin holdings between consecutive top-$x$ groups, such as between the top-500 and top-1000, and so forth. The right plot displays the decentralization degree over time.
• Figure 3: Spearman coefficient analysis of BTC top-ranking addresses. (1) Spearman coefficients for different day intervals for top-5000 addresses. (2) Breakdown of Spearman coefficients across different ranking groups with 1 Day interval. A higher Spearman coefficient indicates greater stability.
• Figure 4: Retention rate analysis of BTC top-ranking addresses: (1) Retention rates for different day intervals for top-5000 addresses. (2) Breakdown of retention rates across different ranking groups with 1 Day interval. A higher retention rate indicates a greater proportion of addresses remain.
• Figure 5: HHI decentralization degree, with independent entities (orange) and clustered entities (black). Red dashed lines highlight key dates impacting BTC decentralization.