Bidirectional fusion heterogeneous graph networks for semi-supervised Bitcoin transaction anomaly detection in dynamic transaction graphs

Documenti analoghi

• DGAM: Dual-Guided Anomaly Mining for Semi-Supervised Graph Anomaly Detection
  di: Xingxuan Li, et al.
  Pubblicazione: (2026-05-01)
• Decoupling Anomaly Discrimination and Representation Learning: Self-supervised Learning for Anomaly Detection on Attributed Graph
  di: YanMing Hu, et al.
  Pubblicazione: (2024-05-01)
• Graph-Attention-Regularized Deep Support Vector Data Description for Semi-Supervised Anomaly Detection: A Case Study in Automotive Quality Control
  di: Taha J. Alhindi
  Pubblicazione: (2025-12-01)
• Improving Graph Convolutional Network with Learnable Edge Weights and Edge-Node Co-Embedding for Graph Anomaly Detection
  di: Xiao Tan, et al.
  Pubblicazione: (2024-04-01)
• A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks
  di: Jiahui Wang, et al.
  Pubblicazione: (2024-01-01)