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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https://doi.org/10.1007/978-981-97-4430-5Blockchain; Network science; Data analytics; Data mining; Graph mining; Behavior analysis

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978-981-97-4432-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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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

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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

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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