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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書目名稱Blockchain Transaction Data Analytics影響因子(影響力)




書目名稱Blockchain Transaction Data Analytics影響因子(影響力)學科排名




書目名稱Blockchain Transaction Data Analytics網絡公開度




書目名稱Blockchain Transaction Data Analytics網絡公開度學科排名




書目名稱Blockchain Transaction Data Analytics被引頻次




書目名稱Blockchain Transaction Data Analytics被引頻次學科排名




書目名稱Blockchain Transaction Data Analytics年度引用




書目名稱Blockchain Transaction Data Analytics年度引用學科排名




書目名稱Blockchain Transaction Data Analytics讀者反饋




書目名稱Blockchain Transaction Data Analytics讀者反饋學科排名

ARM

杠桿

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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云狀

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

富饒

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