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Entropy
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Blockchain and Cryptocurrency Complexity
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Blockchain and Cryptocurrency Complexity

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Statistical Physics".

Deadline for manuscript submissions: closed (18 February 2024) | Viewed by 8286

Special Issue Editors

Dr. Marco Alberto Javarone

E-Mail Website
Guest Editor
1. Department of Physics, University of Bari, 70121 Bari, Italy
2. Centro Ricerche Enrico Fermi, 00184 Roma, Italy
3. Centre for Blockchain Technologies, University College London, London WC1H 0AY, UK
Interests: statistical physics; complex systems; evolutionary game theory; theoretical neuroscience; mathematical physics
Gianni Valerio Vinci

E-Mail
Guest Editor Assistant
Dipartimento di Fisica, University di Roma “Tor Vergata”, 00133 Rome, Italy
Interests: dynamical systems; computational neuroscience; complex systems
Gabriele Di Antonio

E-Mail
Guest Editor Assistant
Centro Ricerche Enrico Fermi, Università degli Studi Roma Tre, 00184 Rome, Italy
Interests: dynamical systems; computational neuroscience; complex systems

Special Issue Information

Dear Colleagues,

Bitcoin represents the most important application of blockchain. This technology has rapidly expanded and led to the emergence of a rich ecosystem composed of many cryptocurrencies, whose complexity impacted society at different levels and gained the attention of scientific communities, from physicists to mathematicians, computer scientists, and economists.

Understanding and forecasting the dynamics of cryptocurrencies is a fascinating challenge; the goal of this Special Issue is to collect relevant contributions in this field. Due to their potential in unveiling and describing complex phenomena, particular interest is oriented towards methods based on statistical physics, machine learning, and their combination.

For instance, network theory and deep learning, which have been proven to be successful in investigating this technology, can be exploited the study of many other aspects related to the dynamics of cryptocurrencies, their behaviour, and their interactions with other systems and technologies.

Contributions to this Special Issue are expected to shed light on the above issues and many others related to the world of blockchain, bitcoin, and cryptocurrencies, proposing original ideas and innovative approaches.

Dr. Marco Alberto Javarone
Guest Editor

Gianni Valerio Vinci
Gabriele Di Antonio
Guest Editor Assistants

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

24 pages, 2867 KiB  
Article
Bitcoin Money Laundering Detection via Subgraph Contrastive Learning
by Shiyu Ouyang, Qianlan Bai, Hui Feng and Bo Hu
Entropy 2024, 26(3), 211; https://doi.org/10.3390/e26030211 - 28 Feb 2024
Cited by 5 | Viewed by 4842
Abstract
The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit [...] Read more.
The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address–transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others. Full article
(This article belongs to the Special Issue Blockchain and Cryptocurrency Complexity)
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14 pages, 5857 KiB  
Article
Illegal Community Detection in Bitcoin Transaction Networks
by Dany Kamuhanda, Mengtian Cui and Claudio J. Tessone
Entropy 2023, 25(7), 1069; https://doi.org/10.3390/e25071069 - 16 Jul 2023
Cited by 1 | Viewed by 2754
Abstract
Community detection is widely used in social networks to uncover groups of related vertices (nodes). In cryptocurrency transaction networks, community detection can help identify users that are most related to known illegal users. However, there are challenges in applying community detection in cryptocurrency [...] Read more.
Community detection is widely used in social networks to uncover groups of related vertices (nodes). In cryptocurrency transaction networks, community detection can help identify users that are most related to known illegal users. However, there are challenges in applying community detection in cryptocurrency transaction networks: (1) the use of pseudonymous addresses that are not directly linked to personal information make it difficult to interpret the detected communities; (2) on Bitcoin, a user usually owns multiple Bitcoin addresses, and nodes in transaction networks do not always represent users. Existing works on cluster analysis on Bitcoin transaction networks focus on addressing the later using different heuristics to cluster addresses that are controlled by the same user. This research focuses on illegal community detection containing one or more illegal Bitcoin addresses. We first investigate the structure of Bitcoin transaction networks and suitable community detection methods, then collect a set of illegal addresses and use them to label the detected communities. The results show that 0.06% of communities from daily transaction networks contain one or more illegal addresses when 2,313,344 illegal addresses are used to label the communities. The results also show that distance-based clustering methods and other methods depending on them, such as network representation learning, are not suitable for Bitcoin transaction networks while community quality optimization and label-propagation-based methods are the most suitable. Full article
(This article belongs to the Special Issue Blockchain and Cryptocurrency Complexity)
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