IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v600y2022ics0378437122003600.html
   My bibliography  Save this article

Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction

Author

Listed:
  • Lin, Dan
  • Wu, Jiajing
  • Xuan, Qi
  • Tse, Chi K.

Abstract

Blockchain is an emerging technology which has attracted wide attention in recent years. As one of the blockchain applications, cryptocurrency has developed rapidly in recent years, attracting criminals to commit fraud and money laundering. Therefore, to better protect the legitimate interests of users and help formulate an effective supervision, it is necessary to track and follow transaction records on blockchain-based systems. This paper studies the problem of transaction tracking in Ethereum from a network perspective, aiming to study explainable strategies for money flow generation. We first collect the space-intensive transaction data from Ethereum blockchain and model them as temporal weighted multi-digraphs. A variety of tracking strategies considering different transaction factors (i.e., frequency and amount) are proposed, and the corresponding random-walk based link predictions method are designed for evaluation. Our method gets explainable results from the experiments, demonstrating that both transaction frequency and amount influence the generation of new transactions in Ethereum. This means when tracking the money flow among Ethereum accounts, we should pay more attention to those transaction paths having a shorter time interval and a larger amount. From these transaction features, the proposed random-walk based link prediction framework is found to be an effective method for transaction tracking. Furthermore, we show an application of transaction tracking via link prediction effectively enhance the ability to detect the suspicious accounts in Ethereum.

Suggested Citation

  • Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    • Handle: RePEc:eee:phsmap:v:600:y:2022:i:c:s0378437122003600
    DOI: 10.1016/j.physa.2022.127504
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122003600
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127504?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Göbel, F. & Jagers, A. A., 1974. "Random walks on graphs," Stochastic Processes and their Applications, Elsevier, vol. 2(4), pages 311-336, October.
    2. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    3. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," Papers 1808.08585, arXiv.org.
    4. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-18, August.
    5. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    6. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    7. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    8. Mark Weber & Giacomo Domeniconi & Jie Chen & Daniel Karl I. Weidele & Claudio Bellei & Tom Robinson & Charles E. Leiserson, 2019. "Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics," Papers 1908.02591, arXiv.org.
    9. Chen, Jialan & Lin, Dan & Wu, Jiajing, 2022. "Do cryptocurrency exchanges fake trading volumes? An empirical analysis of wash trading based on data mining," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mueller, Falko, 2023. "Link and edge weight prediction in air transport networks — An RNN approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    2. Mishra, Shivansh & Singh, Shashank Sheshar & Kumar, Ajay & Biswas, Bhaskar, 2022. "ELP: Link prediction in social networks based on ego network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    3. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    4. Yu, Jiating & Wu, Ling-Yun, 2022. "Multiple Order Local Information model for link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    5. Charikhi, Mourad, 2024. "Association of the PageRank algorithm with similarity-based methods for link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    6. Xing Li & Qingsong Li & Wei Wei & Zhiming Zheng, 2022. "Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities," Mathematics, MDPI, vol. 10(23), pages 1-13, December.
    7. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    8. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    9. Chunning Wang & Fengqin Tang & Xuejing Zhao, 2023. "LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks," Mathematics, MDPI, vol. 11(14), pages 1-15, July.
    10. Xu-Wen Wang & Lorenzo Madeddu & Kerstin Spirohn & Leonardo Martini & Adriano Fazzone & Luca Becchetti & Thomas P. Wytock & István A. Kovács & Olivér M. Balogh & Bettina Benczik & Mátyás Pétervári & Be, 2023. "Assessment of community efforts to advance network-based prediction of protein–protein interactions," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    11. Pei, Panpan & Liu, Bo & Jiao, Licheng, 2017. "Link prediction in complex networks based on an information allocation index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 1-11.
    12. Peng Liu & Liang Gui & Huirong Wang & Muhammad Riaz, 2022. "A Two-Stage Deep-Learning Model for Link Prediction Based on Network Structure and Node Attributes," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    13. Lee, Yan-Li & Dong, Qiang & Zhou, Tao, 2021. "Link prediction via controlling the leading eigenvector," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    14. Guo, Wei-Feng & Zhang, Shao-Wu, 2016. "A general method of community detection by identifying community centers with affinity propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 508-519.
    15. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.
    16. Moradabadi, Behnaz & Meybodi, Mohammad Reza, 2016. "Link prediction based on temporal similarity metrics using continuous action set learning automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 361-373.
    17. Yelai Feng & Huaixi Wang & Chao Chang & Hongyi Lu, 2022. "Intrinsic Correlation with Betweenness Centrality and Distribution of Shortest Paths," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    18. Kai Yang & Yuan Liu & Zijuan Zhao & Xingxing Zhou & Peijin Ding, 2023. "Graph attention network via node similarity for link prediction," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(3), pages 1-10, March.
    19. Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023. "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    20. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:600:y:2022:i:c:s0378437122003600. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.