Name
Abstract | ||
|---|---|---|
Smart contracts, computer protocols designed for autonomous execution on predefined conditions, arise from the evolution of the Bit-coinu0027s 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. |
Year | Venue | DocType |
|---|---|---|
2017 | european conference on machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jérémy Henri J. Charlier | 1 | 0 | 0.34 |
| Sofiane Lagraa | 2 | 28 | 7.48 |
| Radu State | 3 | 623 | 86.87 |
| Jérôme François | 4 | 170 | 21.81 |