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Profiling Smart Contracts Interactions with Tensor Decomposition and Graph Mining

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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.
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Dates and versions

hal-01636450 , version 1 (16-11-2017)

Identifiers

  • HAL Id : hal-01636450 , version 1

Cite

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⟩
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