Abstract
In this chapter, we introduce an overview of network construction in blockchain. In recent years, network has been widely used to present information in various areas, and graph-embedding techniques have attracted attention from various fields. Ethereum is a blockchain-based platform supporting smart contracts. The open nature of blockchain makes the transaction data on Ethereum completely public, and also brings unprecedented opportunities for the transaction network analysis. We first model the Ethereum transaction records as a complex network named temporal weighted multidigraph (TWMDG) by incorporating time and amount features of the transactions, and then define the problem of temporal weighted multidigraph embedding (T-EDGE) by incorporating both temporal and weighted information of the edges. Moreover, we also design several flexible temporal walk strategies for random-walk based graph representation of this large-scale network and study the Ethereum transaction tracking problem and the evolution factors of transaction network via link prediction from the network perspective.
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References
Alqassem, I., Rahwan, I., & Svetinovic, D. (2018). The anti-social system properties: Bitcoin network data analysis. IEEE Transactions on Systems Man & Cybernetics Systems, 50, 1–11
Alqassem, I., Rahwan, I., & Svetinovic, D. (2020). The anti-social system properties: Bitcoin network data analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(1), 21–31
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D. U. (2006). Complex networks: Structure and dynamics. Complex Systems and Complexityence, 424(4–5), 175–308.
Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018). A comprehensive survey of graph embedding: Problems, techniques and applications. IEEE Transactions on Knowledge and Data Engineering, 30(9), 1616–1637.
Chainalysis. Chainalysis. https://www.chainalysis.com/
Chen, T., Zhu, Y., Li, Z., Chen, J., Li, X., Luo, X., et al. (2018a). Understanding Ethereum via graph analysis. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications (pp. 1484–1492). Piscataway, NJ: IEEE.
Chen, W., Zheng, Z., Cui, J., Ngai, E., & Zhou, Y. (2018b). Detecting Ponzi schemes on Ethereum: Towards healthier blockchain technology. In the 2018 World Wide Web Conference.
Chen, W., Wu, J., Zheng, Z., Chen, C., & Zhou, Y. (2019). Market manipulation of bitcoin: Evidence from mining the Mt. Gox transaction network. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications (pp. 964–972). Piscataway, NJ: IEEE.
Chen, W., & Zheng, Z. (2018). Blockchain data analysis: A review of status, trends and challenges. Journal of Computer Research and Development, 055(009), 1853–1870.
CipherTrace. Ciphertrace. https://ciphertrace.com/
ElBahrawy, A., Alessandretti, L., Rusnac, L., Goldsmith, D., Teytelboym, A., & Baronchelli, A. (2019). Collective dynamics of dark web marketplaces. arXiv, abs/1911.09536.
Ethereum. Etherscan. https://etherscan.io/
Feder, A., Gandal, N., Hamrick, J. T., & Moore, T. (2018). The impact of DDoS and other security shocks on Bitcoin currency exchanges: Evidence from Mt. Gox. Journal of Cybersecurity, 3(2), 137–144, 01 . ISSN 2057-2085. https://doi.org/10.1093/cybsec/tyx012
Grover, A., & Leskovec, J. (2016). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 855–864).
Jiang, Z., & Liang, J. (2016). Cryptocurrency portfolio management with deep reinforcement learning.
Kumar, A., Singh, S. S., Singh, K., & Biswas, B. (2020). Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and Its Applications, 553, 124289.
Liang, J., Li, L., Zeng, D., & Raul, H. M. A. (2018). Evolutionary dynamics of cryptocurrency transaction networks: An empirical study. Plos One, 13(8), e0202202.
Lin, D., Wu, J., Yuan, Q., & Zheng, Z. (2020). Modeling and understanding Ethereum transaction records via a complex network approach. IEEE Transactions on Circuits and Systems II-Express Briefs, 67(11), 2737–2741.
Liu, J., & Ye, Y. (2001). Introduction to e-commerce agents: Marketplace marketplace solutions, security issues, and supply and demand. Berlin: Springer.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013a). Efficient estimation of word representations in vector space. Preprint, arXiv:1301.3781.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013b, December). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26 (pp. 3111–3119). Lake Tahoe, NV: Curran Associates, Inc.
O’Neill, P. H. (2017). The curious case of the missing Mt. Gox bitcoin fortune.
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 701–710).
Rehurek, R., & Sojka, P. (2010). Software framework for topic modelling with large corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks (pp. 45–50).
Ron, D., & Shamir, A. (2013). Quantitative analysis of the full bitcoin transaction graph. In International Conference on Financial Cryptography and Data Security (pp. 6–24). Berlin: Springer.
Chainalysis Team. (2017). The rise of cybercrime on Ethereum.
THUNLP (2017). Openne: An open source toolkit for network embedding. https://github.com/thunlp/openne
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Lin, D., Wu, J., Yuan, Q., Zheng, Z. (2021). Analysis and Mining of Blockchain Transaction Network. In: Zheng, Z., Dai, HN., Wu, J. (eds) Blockchain Intelligence. Springer, Singapore. https://doi.org/10.1007/978-981-16-0127-9_3
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DOI: https://doi.org/10.1007/978-981-16-0127-9_3
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