GIST
发表于 2025-3-25 06:29:24
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airborne
发表于 2025-3-25 08:19:49
https://doi.org/10.1007/978-981-97-4430-5Blockchain; Network science; Data analytics; Data mining; Graph mining; Behavior analysis
Engaging
发表于 2025-3-25 13:32:15
978-981-97-4432-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
自传
发表于 2025-3-25 16:42:24
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调整校对
发表于 2025-3-25 23:09:43
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idiopathic
发表于 2025-3-26 02:19:39
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
Forage饲料
发表于 2025-3-26 06:08:46
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faucet
发表于 2025-3-26 11:20:17
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intimate
发表于 2025-3-26 15:12:25
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
使无效
发表于 2025-3-26 19:35:33
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