TY - GEN
T1 - Dissecting ethereum blockchain analytics
T2 - 2020 SIAM International Conference on Data Mining, SDM 2020
AU - Li, Yitao
AU - Islambekov, Umar
AU - Akcora, Cuneyt
AU - Smirnova, Ekaterina
AU - Gel, Yulia R.
AU - Kantarcioglu, Murat
N1 - Publisher Copyright:
© 2020 by SIAM.
PY - 2020
Y1 - 2020
N2 - The Blockchain technology and, in particular blockchain-based cryptocurrencies, offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. This allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties and crypto price dynamics. The goal of this paper is to facilitate our understanding on horizons and limitations of what can be learned on crypto-tokens from local topology and geometry of the Ethereum transaction network whose even global network properties remain scarcely explored. By introducing novel tools based on Topological Data Analysis and Functional Data Depth into Blockchain Data Analytics, we show that Ethereum network (one of the most popular blockchains for creating new crypto-tokens) can provide critical insights on price changes of crypto-tokens that are otherwise largely inaccessible with conventional data sources and traditional analytic methods.
AB - The Blockchain technology and, in particular blockchain-based cryptocurrencies, offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. This allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties and crypto price dynamics. The goal of this paper is to facilitate our understanding on horizons and limitations of what can be learned on crypto-tokens from local topology and geometry of the Ethereum transaction network whose even global network properties remain scarcely explored. By introducing novel tools based on Topological Data Analysis and Functional Data Depth into Blockchain Data Analytics, we show that Ethereum network (one of the most popular blockchains for creating new crypto-tokens) can provide critical insights on price changes of crypto-tokens that are otherwise largely inaccessible with conventional data sources and traditional analytic methods.
UR - http://www.scopus.com/inward/record.url?scp=85087794286&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087794286&partnerID=8YFLogxK
U2 - 10.1137/1.9781611976236.59
DO - 10.1137/1.9781611976236.59
M3 - Conference contribution
AN - SCOPUS:85087794286
T3 - Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020
SP - 523
EP - 531
BT - Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020
A2 - Demeniconi, Carlotta
A2 - Chawla, Nitesh
PB - Society for Industrial and Applied Mathematics Publications
Y2 - 7 May 2020 through 9 May 2020
ER -