  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.
