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Numerous studies have been conducted to investigate the properties oflarge-scale temporal graphs. Despite the ubiquity of these graphs in real-worldscenarios, it's usually impractical for us to obtain the whole real-time graphsdue to privacy concerns and technical limitations. In this paper, we introducethe concept of {\it Live Graph Lab} for temporal graphs, which enables open,dynamic and real transaction graphs from blockchains. Among them, Non-fungibletokens (NFTs) have become one of the most prominent parts of blockchain overthe past several years. With more than \$40 billion market capitalization, thisdecentralized ecosystem produces massive, anonymous and real transactionactivities, which naturally forms a complicated transaction network. However,there is limited understanding about the characteristics of this emerging NFTecosystem from a temporal graph analysis perspective. To mitigate this gap, weinstantiate a live graph with NFT transaction network and investigate itsdynamics to provide new observations and insights. Specifically, throughdownloading and parsing the NFT transaction activities, we obtain a temporalgraph with more than 4.5 million nodes and 124 million edges. Then, a series ofmeasurements are presented to understand the properties of the NFT ecosystem.Through comparisons with social, citation, and web networks, our analyses giveintriguing findings and point out potential directions for future exploration.Finally, we also study machine learning models in this live graph to enrich thecurrent datasets and provide new opportunities for the graph community. Thesource codes and dataset are available at https://livegraphlab.github.io.

Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT