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Blockchain GNN Research

A personal collection of research papers focused on the application of Graph Neural Networks (GNN) to blockchain data.

Outline

  1. Resources
  2. Classics
  3. Ethereum Data and Applications
  4. Subgraphs
  5. Reading List

Resources

  • PyG: PyTorch GNN library
  • NetworkX: Network analysis in Python
  • GNN Intro: A Gentle Introduction to Graph Neural Networks
  • XBlock: A blockchain data platform for academic research
  • Karate Club: Unsupervised machine learning extension library for NetworkX

Classics

graph2vec: Embedding framework to learn data-driven distributed representations of arbitrary sized graphs.

Keywords (paper): Graph Kernels · Deep Learning · Representation Learning

arXiv: https://arxiv.org/abs/1707.05005

Ethereum Data and Applications

Internal and external transaction, smart contract, and token data synced up to block 13,250,000. 

Keywords (paper): Blockchain · Data analytics · Ethereum · Smart contracts
Keywords (personal): External transactions · Internal transactions · Contract info · ERC20 transactions · ERC721 transactions

Data repository: http://xblock.pro/xblock-eth.html

IEEE: https://ieeexplore.ieee.org/document/9086439
Constructs MFG, CGG and CIG graphs as bases for data collection and analysis and analysis techniques.

Keywords (personal): Money flow graph · Contract creation graph · Contract invocation graph · Graph analysis

IEEE: https://ieeexplore.ieee.org/document/8486401

Subgraphs

Improved Graph2Vec: Integrating Ether flow information into transaction subgraphs.

Keywords (paper): Blockchain · Ethereum · Phising detection · Graph classification
Keywords (personal): Subgraph embeddings · Graph2Vec · line model

Springer: https://link.springer.com/chapter/10.1007/978-981-15-9213-3_14
IIGBNN: Learning transaction subgraph embedding for graph classification.

Keywords: Blockchain · Identity Inference · Graph Classification · Graph Neural Network

arXiv: https://arxiv.org/abs/2104.06559

Reading List

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Research papers, experiments, and ideas for the application of GNNs to blockchain data

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