Paper published in a book (Scientific congresses, symposiums and conference proceedings)
Profiling Smart Contracts Interactions Tensor Decomposition and Graph Mining.
CHARLIER, Jérémy Henri J.; LAGRAA, Sofiane; STATE, Raduet al.
2017 • In Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017) co-located with the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), Skopje, Macedonia, September 18, 2017.
[en] Smart contracts, computer protocols designed for autonomous execution on predefined conditions, arise from the evolution of the Bitcoin’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 stochastic 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.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN)
Disciplines :
Computer science
Author, co-author :
CHARLIER, Jérémy Henri J. ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
LAGRAA, Sofiane ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
STATE, Radu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Francois, Jerome; INRIA Nancy - Grand Est > Madynes
External co-authors :
yes
Language :
English
Title :
Profiling Smart Contracts Interactions Tensor Decomposition and Graph Mining.
Publication date :
September 2017
Event name :
Second Workshop on MIning DAta for financial applicationS (MIDAS 2017) co-located with the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD} 2017)
Event organizer :
ECML PKDD
Event place :
Skopje, North Macedonia
Event date :
from 18-09-2017 to 22-09-2017
Audience :
International
Main work title :
Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017) co-located with the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), Skopje, Macedonia, September 18, 2017.
Loi Luu, Duc-Hiep Chu, Hrishi Olickel, Prateek Saxena, and Aquinas Hobor. Making smart contracts smarter. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pages 254-269. ACM, 2016.
Massimo Bartoletti and Livio Pompianu. An empirical analysis of smart contracts: platforms, applications, and design patterns. arXiv preprint arXiv:1703.06322, 2017.
Vincenzo Morabito. Smart contracts and licensing. In Business Innovation Through Blockchain, pages 101-124. Springer, 2017.
Florian Idelberger, Guido Governatori, Rffiegis Riveret, and Giovanni Sartor. Evaluation of logic-based smart contracts for blockchain systems. 2016.
Merit Kõlvart, Margus Poola, and Addi Rull. Smart contracts. In The Future of Law and etechnologies, pages 133-147. Springer, 2016.
Tamara G Kolda and Brett W Bader. Tensor decompositions and applications. SIAM review, 51(3):455-500, 2009.
Richard A Harshman. Foundations of the parafac procedure: models and conditions for an" explanatory" multimodal factor analysis. 1970.
J Douglas Carroll and Jih-Jie Chang. Analysis of individual diffierences in multidimensional scaling via an n-way generalization of eckart-young decomposition. Psychometrika, 35(3):283-319, 1970.
Anders H Andersen and William S Rayens. Structure-seeking multilinear methods for the analysis of fmri data. NeuroImage, 22(2):728-739, 2004.
Bhaskar Sen and Keshab K Parhi. Extraction of common task signals and spatial maps from group fmri using a parafac-based tensor decomposition technique. In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, pages 1113-1117. IEEE, 2017.
Evrim Acar, Seyit Ahmet Camtepe, Mukkai S Krishnamoorthy, and Bulent Yener. Modeling and multiway analysis of chatroom tensors. ISI, 2005:256-268, 2005.
Yanning Shen, Brian Baingana, and Georgios B Giannakis. Inferring directed network topologies via tensor factorization. In Signals, Systems and Computers, 2016 50th Asilomar Conference on, pages 1739-1743. IEEE, 2016.
Daniel D Lee and H Sebastian Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788-791, 1999.
Dilip B Madan, Peter P Carr, and Eric C Chang. The variance gamma process and option pricing. Review of Finance, 2(1):79-105, 1998.
John C Hull. Options, futures, and other derivatives. Pearson Education India, 2006.
Linton C Freeman. Centrality in social networks conceptual clariffication. Social networks, 1(3):215-239, 1978.
Yun Zhang, Charles A. Phillips, Gary L. Rogers, Erich J. Baker, Elissa J. Chesler, and Michael A. Langston. On ffinding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types. BMC Bioinformatics, 15(1):110, 2014.
Gabriela Alexe, Sorin Alexe, Yves Crama, Stephan Foldes, Peter L Hammer, and Bruno Simeone. Consensus algorithms for the generation of all maximal bicliques. Discrete Applied Mathematics, 145(1):11-21, 2004.
Glen Jeh and Jennifer Widom. Simrank: a measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 538-543. ACM, 2002.
Angela Loregian, Lorenzo Mercuri, and Edit Rroji. Approximation of the variance gamma model with a ffinite mixture of normals. Statistics & Probability Letters, 82(2):217-224, 2012.
