Detecting Anomalies on Cryptocurrency Markets Using Graph Algorithms

Agata Skorupka

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Paper

Detecting Anomalies on Cryptocurrency Markets Using Graph Algorithms

Topics: Big Data Applications; Data Analytics; Data Science; Deep Learning; Feature Selection; Predictive Modeling; Social Data Analytics; Social Web Search and Mining; Statistics Exploratory Data Analysis; Web Analytics

In Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1, 504-509, 2023 , Rome, Italy

Author: Agata Skorupka

Affiliation: Collegium of Economic Analysis, Warsaw School of Economics, Warsaw, Poland

Keyword(s): Graph Embeddings, Anomaly, Anomaly Detection, Cryptocurrency.

Abstract: The low level regulation of cryptocurrency market as well as crucial role of trust and digital market specificity makes it a good environment for anonymous transactions without identity verification, therefore fraudulent activities. Examples of such anomalies may be failing to fulfil transaction, as well as different forms of market manipulation. As cryptocurrencies are incorporated in more and more investment portfolios, including big companies accepting payment by this means, anomalies on cryptocurrency may pose significant systemic risk. Therefore there is a need to detect fraudulent users in a computationally efficient way. This paper presents usage of graph algorithms for that purpose. While most of the literature is focused on using structural and classical embeddings, this research proposes utilizing nodes statistics to build an accurate model with less engineering overhead as well as computational time involved.

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Paper citation in several formats:

Skorupka, A. (2023). Detecting Anomalies on Cryptocurrency Markets Using Graph Algorithms. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-664-4; ISSN 2184-285X, SciTePress, pages 504-509. DOI: 10.5220/0012133900003541

@conference{data23,
author={Agata Skorupka},
title={Detecting Anomalies on Cryptocurrency Markets Using Graph Algorithms},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA},
year={2023},
pages={504-509},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012133900003541},
isbn={978-989-758-664-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA
TI - Detecting Anomalies on Cryptocurrency Markets Using Graph Algorithms
SN - 978-989-758-664-4
IS - 2184-285X
AU - Skorupka, A.
PY - 2023
SP - 504
EP - 509
DO - 10.5220/0012133900003541
PB - SciTePress