Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (1): 328-335.doi: 10.11896/jsjkx.201200118

• Information Security • Previous Articles     Next Articles

Visual Analysis Method of Blockchain Community Evolution Based on DPoS Consensus Mechanism

WEN Xiao-lin, LI Chang-lin, ZHANG Xin-yi, LIU Shang-song, ZHU Min   

  1. College of Computer Science,Sichuan University,Chengdu 610065,China
  • Received:2020-12-11 Revised:2021-04-23 Online:2022-01-15 Published:2022-01-18
  • About author:WEN Xiao-lin,born in 1998,postgra-duate,is a member of China Computer Federation.His main research interests include information visualization and visual analytics.
    ZHU Min,born in 1971,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include information visualization,visual analytics and bioinformatics.
  • Supported by:
    Chengdu Science and Technology Bureau,China(2019-YF05-02121-SN).

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Abstract: DPoS (delegated proof of stake) is one of the current mainstream blockchain consensus mechanisms,and the unique node election mechanism makes it form an evolving blockchain community.Analyzing the evolution model of the blockchain community can discover the potential risks of the consensus mechanism,which has very important research significance.For the DPoS consensus mechanism blockchain data,a novel combination analysis method of the consensus mechanism effectiveness is proposed,and a set of visual analysis methods are designed to help users analyze the evolutionary model of the blockchain community from multiple angles.First,it quantifies the difference between the degree of completion of the work and the voting ranking before and after the node ranking change and analyzes the selection efficiency and incentive efficiency of the consensus mechanism;then,it focuses on the combined efficiency of the consensus mechanism,the evolution of the geographical distribution of nodes,and the comparison of the evolutionary differences between nodes and designs visual views and interactive means;finally,it designs and implements a visual analysis system of blockchain community evolution based on the DPoS consensus mechanism based on the real data of the EOS main chain and verifies the usability and effectiveness of this method through case studies and expert evaluation.

Key words: Blockchain, Community evolution, Consensus mechanism, DPoS, Visual analysis

CLC Number: 

  • TP391.41
[1]陈为,沈则潜,陶煜波.数据可视化(第2版)[M].北京:电子工业出版社,2019.
[2]ZHENG Z,XIE S,DAI H N,et al.Blockchain Challenges and Opportunities:A survey[J].International Journal of Web and Grid Services,2018,14(4):352-375.
[3]BANO S,SONNINO A,Al-BASSAM M,et al.SoK:Consensus in the Age of Blockchains[C]//Proceedings of the 1st ACM Conference on Advances in Financial Technologies.2019:183-198.
[4]ZOHAR A.Securing and Scaling Cryptocurrencies[C]//Twenty-Sixth International Joint Conference on Artificial Intelligence.2017:5161-5165.
[5]FERDOUS M S,CHOWDHURY M J M,HOQUE M A,et al.Blockchain Consensus Algorithms:A Survey[J].arXiv:2001.07091,2020.
[6]GUO S T,WANG R J,ZHANG F L.Summary of Principle and Application of Blockchain[J].Computer Science,2021,48(2):271-281.
[7]TAN S P,YANG C.Research and Improvement of Blockchain's DPoS Consensus Mechanism[J].Modern Computer (Professio-nal Edition),2019(6):4.
[8]HUANG J C,XU X H,WANG S C.Improved Scheme of Dele-gated Proof of Stake Consensus Mechanism[J].Journal of Computer Applications,2019,39(7):2162-2167.
[9]JI Y X,HUANG J H,WANG Z,et al.Improved PBFT Consensus Algorithm Based on Trust Matching[J].Computer Science,2021,48(2):303-310.
[10]CHEN M R,LIN Y,LAN W,et al.Improvement of DPoS Consensus Mechanism Based on Positive Incentive[J].Computer Science,2020,47(2):269-275.
[11]TOVANICH N,HEULOT N,FEKETE J D,et al.Visualization of Blockchain Data:A Systematic Review[J].IEEE Transactions on Visualization and Computer Graphics,2019,27(7):3135-3152.
[12]BATTISTA G D,DONATO V D,PATRIGNANI M,et al.Bit-coneview:Visualization of Flows in the Bitcoin Transaction Graph[C]//Visualization for Cyber Security.IEEE,2015:1-8.
[13]DAN M G,BIRCH D,AKROYD D,et al.Visualizing Dynamic Bitcoin Transaction Patterns[J].Big Data,2016,4(2):109-119.
[14]GAULDIE D,LANGEVIN S,SCHRETLEN P,et al.Louvain Clustering for Big Data Graph Visual Analytics[C]//IEEE Visweek 2013.IEEE,2013.
[15]ISENBERG P,KINKELDEY C,FEKETE J D.Exploring entity behavior on the bitcoin blockchain [C]//VIS 2017-IEEE Conference on Visualization.2017:1-2.
[16]SUN Y,XIONG H,YIU S,et al.BitVis:An Interactive Visua-lization System for Bitcoin Accounts Analysis[C]//2019 Crypto Valley Conference on Blockchain Technology (CVCBT).IEEE,2019:21-25.
[17]YUE X W,SHU X,ZHU X,et al.Bitextract:Interactive Visua-lization for Extracting Bitcoin Exchange Intelligence[J].IEEE Transactions on Visualization and Computer Graphics,2018,25(1):162-171.
[18]CHAWATHE S.Monitoring Blockchains with Self-OrganizingMaps[C]//2018 17th IEEE International Conference On Trust,Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).IEEE,2018:1870-1875.
[19]NORVILL R,PONTIVEROS B B F,STATE R,et al.Visual Emulation for Ethereum's Virtual Machine[C]//NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium.IEEE,2018:1-4.
[20]AHMED M,SHUMAILOV I,ANDERSON R.Tendrils ofCrime:Visualizing the Diffusion of Stolen Bitcoins[C]//International Workshop on Graphical Models for Security.Springer,Cham,2018:1-12.
[21]BISTARELLI S,SANTINI F.Go with the-Bitcoin-Flow,withVisual Analytics[C]//Proceedings of the 12th International Conference on Availability,Reliability and Security.2017:1-6.
[22]OGGIER F,PHETSOUVANH S,DATTA A.BiVA:Bitcoinnetwork visualization & analysis[C]//2018 IEEE International Conference on Data Mining Workshops (ICDMW).IEEE,2018:1469-1474.
[23]KUZUNO H,KARAM C.Blockchain explorer:An AnalyticalProcess and Investigation Environment for Bitcoin[C]//2017 APWG Symposium on Electronic Crime Research (eCrime).IEEE,2017:9-16.
[24]KINKELDEY C,FEKETE J D,BLASCHEK T,et al.Visualizing and Analyzing Entity Activity on the Bitcoin Network[J].arXiv:1912.08101,2019.
[25]BOGNER A.Seeing is Understanding:Anomaly Detection inBlockchains with Visualized Features[C]//Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers.2017:5-8.
[26]ZHONG Z,WEI S,ZHAO Y,et al.SilkViser:A Visual Explorer of Blockchain-based Cryptocurrency Transaction Data[J].ar-Xiv:2009.02651,2020.
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