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：大量研究致力于探究大规模时序图的特性。尽管此类图在现实场景中普遍存在，但由于隐私限制和技术局限，获取完整的实时图通常不切实际。本文针对时序图提出"Live Graph Lab"概念，支持从区块链中获取开放、动态且实时的交易图。其中，非同质化代币（NFT）在过去数年间已成为区块链最核心的组成部分之一。这一去中心化生态系统市值超400亿美元，产生海量、匿名的真实交易活动，自然形成复杂的交易网络。然而，从时序图分析视角对该新兴NFT生态系统特征的认识仍十分有限。为弥补这一不足，我们以NFT交易网络为例构建实时图，通过分析其动态特性提供新观察与见解。具体而言，我们通过下载并解析NFT交易活动，获得包含超过450万个节点及1.24亿条边的时序图；进而通过系列度量分析理解NFT生态系统特性。通过与社交网络、引文网络及万维网的对比，本分析揭示了有趣发现并指出未来探索方向。最后，我们还在此实时图上研究机器学习模型，以丰富现有数据集并为图社区提供新机遇。源代码与数据集发布于https://livegraphlab.github.io。