標(biāo)題: Titlebook: Blockchain Transaction Data Analytics; Complex Network Appr Jiajing Wu,Dan Lin,Zibin Zheng Book 2025 The Editor(s) (if applicable) and The [打印本頁] 作者: 法官所用 時間: 2025-3-21 17:04
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(xué)科排名
書目名稱Blockchain Transaction Data Analytics網(wǎng)絡(luò)公開度
書目名稱Blockchain Transaction Data Analytics網(wǎng)絡(luò)公開度學(xué)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(xué)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(xué)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(xué)科排名
作者: ARM 時間: 2025-3-21 21:07 作者: 杠桿 時間: 2025-3-22 02:45
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.作者: 無節(jié)奏 時間: 2025-3-22 07:28 作者: 云狀 時間: 2025-3-22 12:07
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作者: 富饒 時間: 2025-3-22 16:22
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作者: Albinism 時間: 2025-3-22 17:16
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作者: deactivate 時間: 2025-3-22 21:28 作者: Allure 時間: 2025-3-23 05:14 作者: 流逝 時間: 2025-3-23 07:28
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作者: 敏捷 時間: 2025-3-23 09:53 作者: myocardium 時間: 2025-3-23 14:11
Transaction Tracking Based on Personalized PageRank Algorithmn the blockchain and to recover stolen funds from vast transaction data. We model blockchain transaction records as transaction graphs and view blockchain transaction tracking as a graph search task. To achieve efficient and effective tracking of fund transfers in transaction graphs, we propose an s作者: 廣口瓶 時間: 2025-3-23 21:50
J. Scott Weese DVM, DVSC, DIPACVIMion 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作者: Diastole 時間: 2025-3-24 00:53
Shushma Chaturvedi,Joseph R. Bertinoploration 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作者: 易于出錯 時間: 2025-3-24 02:51 作者: 牛馬之尿 時間: 2025-3-24 08:26 作者: 出沒 時間: 2025-3-24 12:06
Reconciling the Past and the Presentsed serious losses. Therefore, it is necessary to classify Ethereum accounts in order to better identify those involved in illegal transactions and analyze the behavior patterns of different classes of accounts. In this chapter, we construct an Ethereum transaction network based on transaction recor作者: incisive 時間: 2025-3-24 15:03
https://doi.org/10.1007/978-1-349-62271-9ublic 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作者: AMEND 時間: 2025-3-24 22:51 作者: NAV 時間: 2025-3-25 02:26
An Abductive Theory of Scientific Method,n the blockchain and to recover stolen funds from vast transaction data. We model blockchain transaction records as transaction graphs and view blockchain transaction tracking as a graph search task. To achieve efficient and effective tracking of fund transfers in transaction graphs, we propose an s作者: GIST 時間: 2025-3-25 06:29 作者: airborne 時間: 2025-3-25 08:19
https://doi.org/10.1007/978-981-97-4430-5Blockchain; Network science; Data analytics; Data mining; Graph mining; Behavior analysis作者: Engaging 時間: 2025-3-25 13:32
978-981-97-4432-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: 自傳 時間: 2025-3-25 16:42 作者: 調(diào)整校對 時間: 2025-3-25 23:09 作者: idiopathic 時間: 2025-3-26 02:19
Dynamic and Microscopic Traits of Typical Accountsghbors and interaction patterns. Additionally, our observations indicate that criminal gangs may be involved in phishing schemes. Based on the conclusions of our account analysis, we designed a variety of account features for classification tasks. Experimental results confirm the utility of our prop作者: Forage飼料 時間: 2025-3-26 06:08 作者: faucet 時間: 2025-3-26 11:20 作者: intimate 時間: 2025-3-26 15:12
Account Classification Based on the Homophily-Heterophily Graph Neural Networks aggregations. Specifically, BPA-GNN consists of three main modules including bi-path neighbor sampling, separated neighborhood aggregation, and attention-based node representation learning. Comprehensive experiments on a real Ethereum transaction dataset demonstrate the state-of-the-art performance作者: 使無效 時間: 2025-3-26 19:35
Phishing Fraud Detection Based on the Streaming Graph Algorithmtures as edge features instead of node features within one task, allowing each transaction to be streamed in 2DynEthNet, aiming to capture the evolutionary features of the Ethereum transaction network at a fine-grained level in continuous time. ., we adopt the strategy of incremental information tra作者: rectocele 時間: 2025-3-27 00:49 作者: ventilate 時間: 2025-3-27 02:24
Transaction Tracking Based on Personalized PageRank Algorithmelational blockchain transaction graphs. Theoretical analysis and experimental results on datasets from multiple blockchain platforms demonstrate that TRacer can perform transaction tracking tasks more efficiently at lower costs and achieve better tracking results compared to existing methods or eve作者: Mettle 時間: 2025-3-27 08:46 作者: defendant 時間: 2025-3-27 12:34
Salvaging Strasberg at the Fin De Siècleaddition, the degree of addresses provides a useful foundation for forecasting the path of incoming transactions. There is a discussion of possible future research on Ethereum transaction link prediction, such as the label effect of center addresses.作者: 厭倦嗎你 時間: 2025-3-27 15:37
https://doi.org/10.1007/978-1-349-62271-9evolves from decentralization to oligopoly. Our method can effectively capture abnormal voting phenomena in EOSIO, which can also provide important insights for the design and maintenance of other DPoS-based blockchains.作者: eucalyptus 時間: 2025-3-27 19:13 作者: 極微小 時間: 2025-3-27 22:12
https://doi.org/10.1007/978-1-349-62271-9tures as edge features instead of node features within one task, allowing each transaction to be streamed in 2DynEthNet, aiming to capture the evolutionary features of the Ethereum transaction network at a fine-grained level in continuous time. ., we adopt the strategy of incremental information tra作者: Detonate 時間: 2025-3-28 04:01 作者: 小爭吵 時間: 2025-3-28 08:20
An Abductive Theory of Scientific Method,elational blockchain transaction graphs. Theoretical analysis and experimental results on datasets from multiple blockchain platforms demonstrate that TRacer can perform transaction tracking tasks more efficiently at lower costs and achieve better tracking results compared to existing methods or eve作者: 套索 時間: 2025-3-28 12:18 作者: 串通 時間: 2025-3-28 16:41
