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Area of Science:

  • Computer Science
  • Data Science
  • Blockchain Technology

Background:

  • Bitcoin's decentralized nature necessitates robust analytical tools.
  • Existing datasets lack the scale and temporal detail for comprehensive blockchain research.
  • Advancements in graph analytics can uncover complex patterns in cryptocurrency transactions.

Purpose of the Study:

  • To present the largest publicly available, temporally annotated graph dataset of Bitcoin transactions.
  • To facilitate research in blockchain analytics, network analysis, and supervised learning.
  • To provide labeled subsets for entity recognition and node classification tasks.

Main Methods:

  • Construction of a large-scale graph dataset with 252 million nodes and 785 million edges.
  • Timestamping of all nodes and edges for temporal analysis.
  • Creation of labeled subsets: 34,000 nodes for entity types and 100,000 addresses for names/types.

Main Results:

  • The dataset is the most extensive resource for Bitcoin transaction network analysis.
  • Baseline performance established for node classification using graph neural networks.
  • Demonstrated applicability in fraud detection, network analysis, and temporal graph learning.

Conclusions:

  • The released dataset significantly advances the field of blockchain analytics.
  • It supports a wide range of applications, from cryptocurrency forensics to temporal network studies.
  • Open access to the dataset, code, and benchmarks promotes further research and development.