Decoding Decentralized Finance Transactions through Ego Network Motif Mining
Natkamon Tovanich, C\'elestin Coquid\'e, R\'emy Cazabet

TL;DR
This paper introduces a method to analyze DeFi transactions by extracting ego network motifs from token transfer networks, enabling identification of smart contract functions and user activities despite data challenges.
Contribution
It proposes a novel motif mining approach to decode DeFi transactions, improving understanding of user and contract interactions in decentralized finance.
Findings
Efficient identification of smart contract methods using network motifs
Insights into user and contract activity patterns in DeFi
Robust analysis despite incomplete or inaccurate data
Abstract
Decentralized Finance (DeFi) is increasingly studied and adopted for its potential to provide accessible and transparent financial services. Analyzing how investors use DeFi is important for reaching a better understanding of their usage and for regulation purposes. However, analyzing DeFi transactions is challenging due to often incomplete or inaccurate labeled data. This paper presents a method to extract ego network motifs from the token transfer network, capturing the transfer of tokens between users and smart contracts. Our results demonstrate that smart contract methods performing specific DeFi operations can be efficiently identified by analyzing these motifs while providing insights into account activities.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Systems and Time Series Analysis · Blockchain Technology Applications and Security · Stock Market Forecasting Methods
