Titlebook: Blockchain Transaction Data Analytics; Complex Network Appr Jiajing Wu,Dan Lin,Zibin Zheng Book 2025 The Editor(s) (if applicable) and The

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书目名称Blockchain Transaction Data Analytics影响因子(影响力)




书目名称Blockchain Transaction Data Analytics影响因子(影响力)学科排名




书目名称Blockchain Transaction Data Analytics网络公开度




书目名称Blockchain Transaction Data Analytics网络公开度学科排名




书目名称Blockchain Transaction Data Analytics被引频次




书目名称Blockchain Transaction Data Analytics被引频次学科排名




书目名称Blockchain Transaction Data Analytics年度引用




书目名称Blockchain Transaction Data Analytics年度引用学科排名




书目名称Blockchain Transaction Data Analytics读者反馈




书目名称Blockchain Transaction Data Analytics读者反馈学科排名

ARM

杠杆

Blockchain Data Analytics from a Network Perspectivee different aspects, i.e., transaction network modeling, transaction network analysis, and network-based detection technology, the purpose being to provide a systematic guideline for researchers in this area.

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云状

Blockchain Data Analytics from a Network Perspectiveion data. These transaction records include rich information and complete traces of financial activities, and therefore provide us an unprecedented opportunity for knowledge discovery. Networks are a universal language for describing interacting real systems, and much work on cryptocurrency transact

富饶

Dynamic and Microscopic Traits of Typical Accountsploration of dynamic and micro-level account characteristics. Therefore, we have conducted pioneering work in this chapter to delve into these aspects of various account types on Ethereum, such as exchanges and phishing entities. Our research involves describing and comparing the trading dynamics of

Albinism

Evolution of Global Driving Factors in Ethereum Transaction Networksnsaction records concentrates on statistical analysis and measurements of the available data. In this chapter, we first collect Ethereum transaction data and create micro-level network models, after which we quantify the influence of network properties on Ethereum evolution using a link-prediction b

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Allure

流逝

Phishing Fraud Detection Based on the Streaming Graph Algorithmublic transaction records on the blockchain as a graph, and then identify phishing addresses through manual feature extraction or graph learning frameworks. Meanwhile, these methods model transactions within a period as a static network for analysis. Therefore, these methods lack the ability to capt