# Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT arXiv: https://arxiv.org/abs/2310.11709 ยท HTML: https://mlsanity.com/paper/2310.11709/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 88 references that are in the map. - First posted on arXiv 18 Oct 2023. ## Builds on (most cited first, 30 of 33) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017) - [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017) - [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014) - [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016) - [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018) - [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019) - [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018) - [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018) - [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016) - [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015) - [Probabilistic Model-Agnostic Meta-Learning](https://mlsanity.com/paper/1806.02817/) (2018) - [ROLAND: Graph Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2208.07239/) (2022) - [Towards Better Evaluation for Dynamic Link Prediction](https://mlsanity.com/paper/2207.10128/) (2022) - [DynGEM: Deep Embedding Method for Dynamic Graphs](https://mlsanity.com/paper/1805.11273/) (2018) - [dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning](https://mlsanity.com/paper/1809.02657/) (2018) - [Hierarchical Graph Pooling with Structure Learning](https://mlsanity.com/paper/1911.05954/) (2019) - [Mapping the NFT revolution: market trends, trade networks and visual features](https://mlsanity.com/paper/2106.00647/) (2021) - [An Overview on Smart Contracts: Challenges, Advances and Platforms](https://mlsanity.com/paper/1912.10370/) (2019) - [Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum Graph](https://mlsanity.com/paper/1912.10105/) (2019) - [NFT Wash Trading: Quantifying suspicious behaviour in NFT markets](https://mlsanity.com/paper/2202.03866/) (2022) - [AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective](https://mlsanity.com/paper/2308.15992/) (2023) - [Blockchain Networks: Data Structures of Bitcoin, Monero, Zcash, Ethereum, Ripple and Iota](https://mlsanity.com/paper/2103.08712/) (2021) - [On the Ethereum Blockchain Structure: a Complex Networks Theory Perspective](https://mlsanity.com/paper/1908.11808/) (2019) - [Networks of Ethereum Non-Fungible Tokens: A graph-based analysis of the ERC-721 ecosystem](https://mlsanity.com/paper/2110.12545/) (2021) - [An Empirical Analysis of Anonymity in Zcash](https://mlsanity.com/paper/1805.03180/) (2018) - [An In-Depth Study of Continuous Subgraph Matching (Complete Version)](https://mlsanity.com/paper/2203.06913/) (2022) - [A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs](https://mlsanity.com/paper/1503.00849/) (2015) - [Survey of Graph Analysis Applications](https://mlsanity.com/paper/1807.00382/) (2018) - [Characterizing EOSIO Blockchain](https://mlsanity.com/paper/2002.05369/) (2020) ## Built on by (most cited first, 10 of 10) - [Position: Graph Foundation Models are Already Here](https://mlsanity.com/paper/2402.02216/) (2024) - [MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs](https://mlsanity.com/paper/2408.09955/) (2024) - [DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs](https://mlsanity.com/paper/2406.12072/) (2024) - [DeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networks](https://mlsanity.com/paper/2511.22314/) (2025) - [InfiCoEvalChain: A Blockchain-Based Decentralized Framework for Collaborative LLM Evaluation](https://mlsanity.com/paper/2602.08229/) (2026) - [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024) - [MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs](https://mlsanity.com/paper/2406.10426/) (2024) - [PulseReddit: A Novel Reddit Dataset for Benchmarking MAS in High-Frequency Cryptocurrency Trading](https://mlsanity.com/paper/2506.03861/) (2025) - [HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling](https://mlsanity.com/paper/2509.03260/) (2025) - [Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols](https://mlsanity.com/paper/2604.20374/) (2026) ## Abstract (arXiv) 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.