Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 502-507.doi: 10.11896/jsjkx.210600178

• Information Security • Previous Articles     Next Articles

Analysis of Bitcoin Entity Transaction Patterns

HE Xi1, HE Ke-tai1, WANG Jin-shan1, LIN Shen-wen2, YANG Jing-lin2, FENG Yu-chao1   

  1. 1 School of Mechanical Engineering,University of Science and Technology Beijing,Beijing 100083,China
    2 National Computer Network Emergency Response Technical Team/Coordination Center of China,Beijing 100000,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:HE Xi,born in 1996,postgraduate.Her main research interests include blockchain technology and Bitcoin anti-anonymity.
    HE Ke-tai,born in 1971,Ph.D,professor.His main research interests include smart logistics and blockchain technology.
  • Supported by:
    National Key Research and Development Program of China(2019QY(Y)0601).

PDF (PC)

698

Abstract: Since the Bitcoin system went online,people have conducted decentralized transfer transactions through Bitcoin addresses,which greatly increased the convenience of transactions,and the transaction records generated by peer-to-peer transactions have always been the focus of research.Due to the huge scale of the Bitcoin transaction network,it takes a long time and huge computing power to explore the entire network directly,and it is also not conducive to observing the internal transaction pattern of the entity.Bitcoin transaction records are permanently stored in the blockchain ledger,and the entity behavior and internal transaction pattern of Bitcoin entity service can be further explored by constructing and analyzing the transaction network.By improving the traditional label propagation algorithm,a label propagation algorithm based on central nodes is proposed to divide the communities of the Bitcoin entity transaction network,and the transaction patterns of the core communities are analyzed,such as Exchanges and mining pools.This paper summarizes two kinds of transaction patterns which are easy to understand and conform to reality.The experimental results prove the differences in transaction patterns within different services,and the graphical display improves the readability of the Bitcoin transaction network.

Key words: Bitcoin, Community detection, Complex network, Label propagation, Transaction pattern

CLC Number: 

  • TP393.02
[1] SATOSHI NAKAMOTO.Bitcoin:A Peer-to-Peer ElectronicCash System[EB/OL].https://bitcoin.org/bitcoin.pdf.
[2] FERRIN D.A preliminary field guide for Bitcoin transactionpatterns[C]//Proceedings of Texas Bitcoin Conference.Austin,Texas,2015:1-8.
[3] TASCA P,LIU S,HAYES A.The evolution of the Bitcoineconomy:Extracting and analyzing the network of payment relationships[J].SSRN Electronic Journal,2018,19(2):94-126.
[4] FLEDER M,KESTER M S,PILLAI S.Bitcoin transactiongraph analysis[J].arXiv:1502.01657,2015.
[5] ALQASSEM I,RAHWAN I,SVETINOVIC D.The anti-social system properties:Bitcoin network data analysis[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2020,50(1):21-31.
[6] DAMIANO D F M,MARINO A,RICCI L.Data-driven analysis of Bitcoin properties:exploiting the users graph[J].InternationalJournal of Data Science and Analytics,2017,6(1):1-18.
[7] LIANG J,LI L,ZENG D.Evolutionary dynamics of cryptocurrency transaction networks:An empirical study[J].Plos One,2018,13(8):e0202202.
[8] MOTAMED A P,BAHRAK B.Quantitative analysis of crypt-ocurrencies transaction graph[J].Applied Network Science,2019,4(1):1-21.
[9] GAIHRE A,YAN L,HANG L.Do Bitcoin users really care about anonymity? An analysis of the Bitcoin transaction graph[C]//2018 IEEE International Conference on Big Data.IEEE,2018:1198-1207.
[10] BLONDEL V D,GUILLAUME J L,LAMBIOTTE R,et al.Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics:Theory and Experiment,2008(10):10008.
[11] OVELGÖNNE M,GEYERSCHULZ A,STEIN M.Randomized greedy modularity optimization for group detection in huge social networks[C]//Proc SNA-KDD'.2010:1-9.
[12] CLAUSET A,NEWMAN M E J,MOORE C.Finding commu-nity structure in very large networks[J].Physical Review E,2004,70(6):6111.
[13] KIRIANOVSKII I,GRANICHIN O,PROSKURNIKOV A.Anew randomized algorithm for community detection in large Networks[J].IFAC-Papers on Line,2016,49(13):31-35.
[14] REMY C,RYM B,MATTHIEU L.Tracking Bitcoin users activity using community detection on a network of weak signals[C]//International Workshop on Complex Networks and Their Applications.2018:166-177.
[15] ZHENG B,ZHU L,SHEN M,et al.Identifying the vulnerabilities of bitcoin anonymous mechanism based on address clustering[J].Science China,2020,63(3):99-113.
[16] ZHENG B,ZHU L,SHEN M,et al.Malicious Bitcoin transaction tracing using incidence relation clustering[C]//Lecture Notes of the Institute for Computer Sciences,Social Informatics and Telecommunications Engineering.Springer International Publishing,2018:313-323.
[17] RAGHAVAN U N,ALBERT R,KUMARA S.Near linear time algorithm to detect community structures in large-scale networks[J].Physical Review E,2007,76(3 Pt 2):036106.
[18] SHEN M,MA Z.A novel node gravitation-based label propagation algorithm for community detection[J].International Journal of Modern Physics C,2019,30(6):1-19.
[19] TANG M,PAN Q,QIAN Y,et al.Parallel label propagation algorithm based on weight and random walk[J].Mathematical Biosciences and Engineering,2021,18(2):1609-1628.
[20] SHANG R,JIAO L,ZHANG W.Circularly searching corenodes based label propagation algorithm for community detection[J].International Journal of Pattern Recognition and Artificial Intelligence,2016,30(8):1659024.1-1659024.22.
[21] ATTAL J P,MALEK M,ZOLGHADRI M.Overlapping community detection using core label propagation algorithm and belonging functions[J].Applied Intelligence,2021(3):8067-8087.
[22] XIA Z,MA T.An improved label propagation algorithm based on node importance and random walk for community detection[J].Modern Physics Letters B Condensed Matter Physics Statistical Physics Applied Physics,2017,31(14):1750162.
[23] KOUNI I,KAROUI W,ROMDHANE L B.Node importancebased label propagation algorithm for overlapping community detection in networks[J].Expert Systems with Applications,2019,162:113020.
[24] NEWMAN M.Modularity and community structure in net-works[J].Proceedings of the National Academy of Sciences,2006,103(23):8577-8582.
[25] MEIKLEJOHN S,POMAROLE M,JORDAN G,et al.A fistful of bitcoins[C]//Proceedings of the 2013 Conference on Internet Measurement Conference.ACM,2013:127-140.
[1] ZHENG Wen-ping, LIU Mei-lin, YANG Gui. Community Detection Algorithm Based on Node Stability and Neighbor Similarity [J]. Computer Science, 2022, 49(9): 83-91.
[2] YANG Bo, LI Yuan-biao. Complex Network Analysis on Curriculum System of Data Science and Big Data Technology [J]. Computer Science, 2022, 49(6A): 680-685.
[3] FU Li-yu, LU Ge-hao, WU Yi-ming, LUO Ya-ling. Overview of Research and Development of Blockchain Technology [J]. Computer Science, 2022, 49(6A): 447-461.
[4] WANG Ben-yu, GU Yi-jun, PENG Shu-fan, ZHENG Di-wen. Community Detection Algorithm Based on Dynamic Distance and Stochastic Competitive Learning [J]. Computer Science, 2022, 49(5): 170-178.
[5] CHEN Shi-cong, YUAN De-yu, HUANG Shu-hua, YANG Ming. Node Label Classification Algorithm Based on Structural Depth Network Embedding Model [J]. Computer Science, 2022, 49(3): 105-112.
[6] YANG Xu-hua, WANG Lei, YE Lei, ZHANG Duan, ZHOU Yan-bo, LONG Hai-xia. Complex Network Community Detection Algorithm Based on Node Similarity and Network Embedding [J]. Computer Science, 2022, 49(3): 121-128.
[7] ZHAO Xue-lei, JI Xin-sheng, LIU Shu-xin, LI Ying-le, LI Hai-tao. Link Prediction Method for Directed Networks Based on Path Connection Strength [J]. Computer Science, 2022, 49(2): 216-222.
[8] PU Shi, ZHAO Wei-dong. Community Detection Algorithm for Dynamic Academic Network [J]. Computer Science, 2022, 49(1): 89-94.
[9] LI Jia-wen, GUO Bing-hui, YANG Xiao-bo, ZHENG Zhi-ming. Disease Genes Recognition Based on Information Propagation [J]. Computer Science, 2022, 49(1): 264-270.
[10] CHEN Xiang-tao, ZHAO Mei-jie, YANG Mei. Overlapping Community Detection Algorithm Based on Subgraph Structure [J]. Computer Science, 2021, 48(9): 244-250.
[11] MU Jun-fang, ZHENG Wen-ping, WANG Jie, LIANG Ji-ye. Robustness Analysis of Complex Network Based on Rewiring Mechanism [J]. Computer Science, 2021, 48(7): 130-136.
[12] HU Jun, WANG Yu-tong, HE Xin-wei, WU Hui-dong, LI Hui-jia. Analysis and Application of Global Aviation Network Structure Based on Complex Network [J]. Computer Science, 2021, 48(6A): 321-325.
[13] WANG Xue-guang, ZHANG Ai-xin, DOU Bing-lin. Non-linear Load Capacity Model of Complex Networks [J]. Computer Science, 2021, 48(6): 282-287.
[14] MA Yuan-yuan, HAN Hua, QU Qian-qian. Importance Evaluation Algorithm Based on Node Intimate Degree [J]. Computer Science, 2021, 48(5): 140-146.
[15] YIN Zi-qiao, GUO Bing-hui, MA Shuang-ge, MI Zhi-long, SUN Yi-fan, ZHENG Zhi-ming. Autonomous Structural Adjustment of Crowd Intelligence Network: Begin from Structure of Biological Regulatory Network [J]. Computer Science, 2021, 48(5): 184-189.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.