The author implementation of the CIKM 2024 short paper:
"LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection".
[Paper] [Poster]

Woochang Hyun, Insoo Lee, Bongwon Suh

Label-Exploring Graph Neural Network (LEX-GNN) is a GNN-based fraud detector that predicts the fraud probability of nodes in a semi-supervised manner and adaptively adjusts the message passing pipeline for improved accuracy.

• Requirements: Python, Torch, and DGL
• Dataset: Yelp and Amazon are loaded from dgl.data.fraud upon code execution.
• Run: python main.py

@inproceedings{hyun2024lex,
  title={LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection},
  author={Hyun, Woochang and Lee, Insoo and Suh, Bongwon},
  booktitle={Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM'24)},
  year={2024}
}