safe-graph/graph-fraud-detection-papers

• highreadme#1

  Clarify repo's role as a curated research collection in the README intro

  原因：

  当前

  A curated list of Graph/Transformer-based papers and resources for fraud, anomaly, and outlier detection.

  复制粘贴的修复

  复制

  This repository is a comprehensive, curated collection of Graph/Transformer-based research papers and resources, specifically designed for researchers and practitioners in fraud, anomaly, and outlier detection.

  待办完成忽略
• highlicense#2

  Add a LICENSE file to the repository

  原因：

  复制粘贴的修复

  复制

  Create a `LICENSE` file in the repository root with the text of a widely recognized open-source license, such as the MIT License, to clearly define usage terms for the repository's contents.

  待办完成忽略
• mediumreadme#3

  Integrate the interactive dashboard and LLM chatbot into the README's introductory value proposition

  原因：

  当前

  A curated list of Graph/Transformer-based papers and resources for fraud, anomaly, and outlier detection.
  
  We have an interactive dashboard to view/filter/search the papers listed in this repo.
  
  To facilitate deep research, we developed a local RAG-based LLM chatbot with 250 publicly accessible papers.

  复制粘贴的修复

  复制

  This repository is a comprehensive, curated collection of Graph/Transformer-based research papers and resources, specifically designed for researchers and practitioners in fraud, anomaly, and outlier detection. To further facilitate deep research, it includes an interactive dashboard for viewing, filtering, and searching papers, and a local RAG-based LLM chatbot with 250 publicly accessible papers.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  What are the best graph neural network and transformer approaches for fraud detection?

  你：未被推荐
  AI 推荐顺序：

  1. GraphSAGE
  2. Heterogeneous Graph Attention Network (HAN)
  3. Relational Graph Convolutional Networks (R-GCN)
  4. Graph Convolutional Networks (GCN)
  5. BERT (Bidirectional Encoder Representations from Transformers)
  6. RoBERTa
  7. Transformer-XL
  8. Longformer
  9. BigBird

  AI 推荐了 9 个替代方案，却始终没点名 safe-graph/graph-fraud-detection-papers。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Where can I find comprehensive research on graph-based anomaly and outlier detection methods?

  你：未被推荐
  AI 推荐顺序：

  1. Google Scholar
  2. arXiv
  3. ACM Digital Library
  4. IEEE Xplore
  5. KDD
  6. ICDM
  7. SDM
  8. AAAI
  9. IJCAI
  10. TKDD
  11. TPAMI
  12. Outlier Analysis by Charu C. Aggarwal
  13. Anomaly Detection: A Survey by Varun Chandola, Arindam Banerjee, and Vipin Kumar

  AI 推荐了 13 个替代方案，却始终没点名 safe-graph/graph-fraud-detection-papers。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of safe-graph/graph-fraud-detection-papers?

  pass
  ⚠AI 未点名 safe-graph/graph-fraud-detection-papers —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts safe-graph/graph-fraud-detection-papers in production, what risks or prerequisites should they evaluate first?

  pass
  ⚠AI 未点名 safe-graph/graph-fraud-detection-papers —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo safe-graph/graph-fraud-detection-papers solve, and who is the primary audience?

  pass
  ⚠AI 未点名 safe-graph/graph-fraud-detection-papers —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？