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

• URL: http://arxiv.org/abs/2310.11709v2
• Date: Thu, 19 Oct 2023 00:57:17 GMT
• Title: Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT
• Authors: Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He
• Abstract summary: We introduce the concept of it Live Graph Lab for temporal graphs, which enables open, dynamic and real transaction graphs from blockchains. We instantiate a live graph with NFT transaction network and investigate its dynamics to provide new observations and insights.
• Score: 28.08921595650609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually impractical for us to obtain the whole real-time graphs due to privacy concerns and technical limitations. In this paper, we introduce the concept of {\it Live Graph Lab} for temporal graphs, which enables open, dynamic and real transaction graphs from blockchains. Among them, Non-fungible tokens (NFTs) have become one of the most prominent parts of blockchain over the past several years. With more than \$40 billion market capitalization, this decentralized ecosystem produces massive, anonymous and real transaction activities, which naturally forms a complicated transaction network. However, there is limited understanding about the characteristics of this emerging NFT ecosystem from a temporal graph analysis perspective. To mitigate this gap, we instantiate a live graph with NFT transaction network and investigate its dynamics to provide new observations and insights. Specifically, through downloading and parsing the NFT transaction activities, we obtain a temporal graph with more than 4.5 million nodes and 124 million edges. Then, a series of measurements are presented to understand the properties of the NFT ecosystem. Through comparisons with social, citation, and web networks, our analyses give intriguing findings and point out potential directions for future exploration. Finally, we also study machine learning models in this live graph to enrich the current datasets and provide new opportunities for the graph community. The source codes and dataset are available at https://livegraphlab.github.io.

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