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.

翻译：大量研究致力于探索大规模时序图的性质。尽管这些图在现实场景中普遍存在，但由于隐私限制和技术瓶颈，全面获取实时图数据往往难以实现。本文提出面向时序图的"实时图实验室"概念，能够从区块链中获取开放、动态的真实交易图。其中，非同质化代币（NFT）在过去几年已成为区块链最突出的组成部分之一。这个去中心化生态系统在市值超过400亿美元的情况下，产生了海量、匿名且真实的交易活动，自然形成了复杂的交易网络。然而，当前从时序图分析视角对新兴NFT生态系统特征的认知仍十分有限。为填补这一空白，我们以NFT交易网络实例化实时图，通过分析其动态特性提供新观察与洞见。具体而言，通过下载并解析NFT交易活动，我们构建了包含超450万个节点和1.24亿条边的时序图。继而通过系列度量分析揭示NFT生态系统特性。与社交网络、引文网络及万维网的对比分析中，我们发现了引人深思的结论，并指出了未来探索方向。最后，我们还在该实时图中研究机器学习模型，以丰富现有数据集并为图社区提供新机遇。源代码与数据集见 https://livegraphlab.github.io。