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Blockchain, a decentralized ledger technology based on cryptographic algorithms, ensures the creation of immutable and tamper-proof ledgers in decentralized systems. The transparent nature of blockchain allows public access to transaction records, providing unprecedented opportunities for blockchain data analytics and mining. The primary value of blockchain transaction data analytics lies in two aspects: 1) by delving into the details of blockchain transaction data, we can extensively explore various types of user behavior patterns and the evolutionary process of blockchain transaction networks; and 2) analyzing blockchain transaction data aids in identifying illicit activities, offering effective regulatory solutions for the establishment of a healthier blockchain ecosystem. This book focuses on data analytics based on network-based approaches, providing a comprehensive analysis of blockchain data analytics problems, key technologies, and future directions.
Different from most existing book, this book takes a unique approach to blockchain data analysis research, focusing on data analytics based on network-based approaches. Leveraging network analysis methods, the book concentrates on three main aspects of blockchain transaction data analytics and mining: (1) transaction network modelling and pattern mining, including macro and micro-level account attributes, money laundering network patterns, and network evolution patterns; (2) account business classification, such as account label prediction based on graph neural networks; and (3) anomaly behavior identification, covering phishing detection, risk scoring, and transaction tracking.
Designed as a valuable resource for students, researchers, engineers, and policymakers in various fields related to blockchain data analytics, this book holds significant importance for understanding blockchain transaction behavior and addressing the detection of illicit activities in the blockchain space.
Jiajing Wu (Senior Member, IEEE) is an Associate Professor in the School of Software Engineering at Sun Yat-sen University. She received her PhD degree from the Department of Electronics and Information Engineering at Hong Kong Polytechnic University in 2014. Dr. Wu has directed over 10 research and development projects and has published over 80 papers in WWW, ISSTA, IEEE TIFS, TCAS, IoTJ, TSMC, etc.. She serves as an Associate Editor of IEEE Transactions on Circuits and Systems II, the guest editor of IEEE Journal on Emerging and Selected Topics in Circuits and Systems, IET Blockchain, Chaos, Sensors. She was the recipient of 6 best or distinguished paper awards, including the best paper of IEEE OJCS and AIBC.
Dan Lin (Graduate Student Member, IEEE) received her B.Eng. in Software Engineering from Sun Yat-sun University, Guangzhou, China, in 2019. She is currently studying toward the Ph.D. degree in the School of Software Engineering, Sun Yat-sen University. Her current research interests include blockchain, cryptocurrency, theories and applications of network science, and anti-money laundering.
Zibin Zheng (Fellow, IEEE) is currently a Professor and the Deputy Dean with the School of Software Engineering, Sun Yat-sen University, Guangzhou, China. He authored or coauthored more than 400 international journal and conference papers, including one ESI hot paper and eleven ESI highly cited papers. According to Google Scholar, his papers have more than 36,000 citations. His research interests include blockchain, software engineering, and services computing. He was the Internetware'24, CCF-ICSS'22, SMDS'21, BlockSys'19 and CollaborateCom16 General Co-Chair, ICSOC'23, CSCloud'23, SC2'19, ICIOT18 and IoV14 PC Co-Chair. He is a Fellow of the IET. He was the recipient of several awards, including ACM Distinguished Member Award, the Top 50 Influential Papers in Blockchain of 2018, the ACM SIGSOFT Distinguished Paper Award at ICSE2010, the Best Student Paper Award at ICWS2010.
Chapter 1. Overview: Blockchain data analytics from a network perspective.- Chapter 2. Dynamic and microscopic traits of typical accounts.- Chapter 3. Evolution of global driving factors in Ethereum transaction networks.- Chapter 4. Evolution and voting behaviors in the EOSIO networks.- Chapter 5.Account classification based on the homophily-heterophily graph neural networks.- Chapter 6. Phishing fraud detection based on the streaming graph algorithm.- Chapter 7. Account risk rating based on network propagation algorithm.- Chapter 8. Transaction tracking based on personalized PageRank algorithm.
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