Bitcoin, launched in 2008, changed how we think about money by allowing people to store and transfer value without a central authority. This shift has led to a massive amount of data being generated, which can be used to understand how Bitcoin works.

        The data includes 252 million nodes and 785 million edges, each with a timestamp. This means every transaction and connection in the Bitcoin network is recorded with a time stamp. This is useful for analyzing how the network changes over time.

        To make this data useful for research, two labeled subsets were created. The first has 34, 000 nodes with entity types, and the second has 100, 000 Bitcoin addresses with entity names and types. These labels help researchers train machine learning models to understand the data better.

        This dataset is the largest of its kind, making it a valuable resource for researchers. It addresses the limitations of smaller datasets and allows for more advanced exploration of Bitcoin's transaction network. The data can be used for various purposes, such as detecting fraud, analyzing the network, and learning from temporal graphs.

        Researchers have already established baseline performance using graph neural network models for node classification tasks. This means they have shown that the data can be used to train models that can classify different types of nodes in the network. The data can be used for more than just Bitcoin research. It can be applied to other areas, such as social network analysis and cybersecurity.

        The complete dataset, along with source code and benchmarks, is available to the public. This means anyone can access the data and use it for their own research. The data can be used to train models that can classify different types of nodes in the network. This is a valuable resource for researchers and developers who want to understand how Bitcoin works and how it can be used for other purposes.

Bitcoin's Big Data: Unraveling Digital Currency Transactions

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