國立虎尾科技大學 |

Blockchain transaction data analytics = complex network approaches /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Blockchain transaction data analytics/ edited by Jiajing Wu, Dan Lin, Zibin Zheng.
其他題名:	complex network approaches /
其他作者:	Zheng, Zibin.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xiv, 203 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	e-Commerce and e-Business. -
電子資源:	https://doi.org/10.1007/978-981-97-4430-5
ISBN:	9789819744305

Blockchain transaction data analytics = complex network approaches /
Blockchain transaction data analyticscomplex network approaches /[electronic resource] :edited by Jiajing Wu, Dan Lin, Zibin Zheng. - Singapore :Springer Nature Singapore :2025. - xiv, 203 p. :ill. (chiefly color), digital ;24 cm. - Big data management,2522-0187. - Big data management..

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.

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.

ISBN: 9789819744305

Standard No.: 10.1007/978-981-97-4430-5doiSubjects--Topical Terms:

1365931
e-Commerce and e-Business.

LC Class. No.: QA76.9.B56

Dewey Class. No.: 005.74

Blockchain transaction data analytics = complex network approaches /
LDR:03704nam a2200361 a 4500 001 1159828
003 DE-He213
005 20241031115731.0
006 m d
007 cr nn 008maaau
008 251029s2025 si s 0 eng d
020 $a 9789819744305 $q (electronic bk.)
020 $a 9789819744299 $q (paper)
024 7 $a 10.1007/978-981-97-4430-5 $2 doi
035 $a 978-981-97-4430-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B56
072 7 $a URY $2 bicssc
072 7 $a UN $2 bicssc
072 7 $a COM093000 $2 bisacsh
072 7 $a URY $2 thema
072 7 $a UN $2 thema
082 0 4 $a 005.74 $2 23
090 $a QA76.9.B56 $b B651 2025
245 0 0 $a Blockchain transaction data analytics $h [electronic resource] : $b complex network approaches / $c edited by Jiajing Wu, Dan Lin, Zibin Zheng.
260 $a Singapore : $c 2025. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xiv, 203 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Big data management, $x 2522-0187
505 0 $a 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.
520 $a 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.
650 2 4 $a e-Commerce and e-Business. $3 1365931
650 2 4 $a Computers and Society. $3 669900
650 2 4 $a Software Engineering. $3 669632
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 1 4 $a Blockchain. $3 1387659
650 0 $a lockchains (Databases) $3 1487113
700 1 $a Zheng, Zibin. $3 1074762
700 1 $a Lin, Dan. $3 883734
700 1 $a Wu, Jiajing. $e editor. $1 https://orcid.org/0000-0001-5155-8547 $3 1360219
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Big data management. $3 1431274
856 4 0 $u https://doi.org/10.1007/978-981-97-4430-5
950 $a Computer Science (SpringerNature-11645)