safe-graph/graph-fraud-detection-papers

• highlicense#1

  Add a LICENSE file to the repository

  Why:

  COPY-PASTE FIX

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  Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).

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• highreadme#2

  Reposition README intro to clarify resource type and chatbot's role

  Why:

  CURRENT

  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.

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  This repository is an **awesome, curated list** of Graph/Transformer-based papers and resources for fraud, anomaly, and outlier detection. It functions as a comprehensive collection, distinct from general search engines or LLMs. We also offer an interactive dashboard to explore these papers. Note: The local RAG-based LLM chatbot mentioned below is a separate tool, not the repository itself.

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• mediumreadme#3

  Add a 'Differentiators' section to the README

  Why:

  COPY-PASTE FIX

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  Add a new section to the README, for example, '## Why this list? (Differentiators)' with text similar to: 'Unlike general academic search engines (e.g., Google Scholar, arXiv), this repository offers a highly specific and **curated collection** of research papers exclusively focused on **Graph/Transformer-based fraud, anomaly, and outlier detection**. Our interactive dashboard and dedicated LLM chatbot further enhance the research experience beyond simple search results.'

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  Where can I find academic papers on graph neural networks for fraud detection?

  you: not recommended
  AI recommended (in order):

  1. Google Scholar
  2. arXiv
  3. IEEE Xplore Digital Library
  4. ACM Digital Library
  5. Semantic Scholar
  6. ResearchGate
  7. KDD
  8. NeurIPS
  9. ICML
  10. AAAI
  11. WWW

  AI recommended 11 alternatives but never named safe-graph/graph-fraud-detection-papers. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  What are the best transformer and deep learning approaches for anomaly detection research?

  you: not recommended
  AI recommended (in order):

  1. GPT-3.5
  2. GPT-4
  3. Llama 2
  4. Mistral
  5. TranAD
  6. OmniAnomaly
  7. Informer
  8. Autoformer
  9. PyTorch-Forecasting
  10. GluonTS
  11. AnoGAN
  12. GANomaly
  13. SimCLR
  14. BYOL

  AI recommended 14 alternatives but never named safe-graph/graph-fraud-detection-papers. This is the gap to close.

  Show full AI answer

• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

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

  pass
  ⚠AI did not name safe-graph/graph-fraud-detection-papers — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

  pass
  ✓AI named safe-graph/graph-fraud-detection-papers explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

  pass
  ⚠AI did not name safe-graph/graph-fraud-detection-papers — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?