Skip to Main content Skip to Navigation
Conference papers

Profiling Smart Contracts Interactions with Tensor Decomposition and Graph Mining

Abstract : Smart contracts, computer protocols designed for autonomous execution on predefined conditions, arise from the evolution of the Bit-coin's crypto-currency. They provide higher transaction security and allow economy of scale through the automated process. Smart contracts provides inherent benefits for financial institutions such as investment banking, retail banking, and insurance. This technology is widely used within Ethereum, an open source block-chain platform, from which the data has been extracted to conduct the experiments. In this work, we propose an multi-dimensional approach to find and predict smart contracts interactions only based on their crypto-currency exchanges. This approach relies on tensor modeling combined with stochas-tic processes. It underlines actual exchanges between smart contracts and targets the predictions of future interactions among the community. The tensor analysis is also challenged with the latest graph algorithms to assess its strengths and weaknesses in comparison to a more standard approach.
Document type :
Conference papers
Complete list of metadata

Cited literature [20 references]  Display  Hide  Download
Contributor : Jérôme François Connect in order to contact the contributor
Submitted on : Thursday, November 16, 2017 - 3:55:51 PM
Last modification on : Saturday, October 16, 2021 - 11:26:07 AM
Long-term archiving on: : Saturday, February 17, 2018 - 1:27:01 PM


Files produced by the author(s)


  • HAL Id : hal-01636450, version 1



Jérémy Charlier, Sofiane Lagraa, Radu State, Jerome Francois. Profiling Smart Contracts Interactions with Tensor Decomposition and Graph Mining. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD) - Workshop on MIning DAta for financial applicationS (MIDAS), Sep 2017, Skopje, Macedonia. ⟨hal-01636450⟩



Record views


Files downloads