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HyperSeq-LLM: Evolving hypergraph sequence learning with LLMs for phishing scam detection on Ethereum

Mingshun Ye et al.

Expert Systems with Applications2026https://doi.org/10.1016/j.eswa.2026.131101article
AJG 1
Weight
0.41

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https://doi.org/https://doi.org/10.1016/j.eswa.2026.131101

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@article{mingshun2026,
  title        = {{HyperSeq-LLM: Evolving hypergraph sequence learning with LLMs for phishing scam detection on Ethereum}},
  author       = {Mingshun Ye et al.},
  journal      = {Expert Systems with Applications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.eswa.2026.131101},
}

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HyperSeq-LLM: Evolving hypergraph sequence learning with LLMs for phishing scam detection on Ethereum

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.