RRepoGEO

REPOGEO REPORT · LITE

safe-graph/GNN-FakeNews

Default branch main · commit 798c2903 · scanned 6/10/2026, 12:43:02 AM

GitHub: 553 stars · 108 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface safe-graph/GNN-FakeNews, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening sentence to clarify purpose

    Why:

    CURRENT
    This repo includes the Pytorch-Geometric implementation of a series of Graph Neural Network (GNN) based fake news detection models.
    COPY-PASTE FIX
    This repository is a comprehensive collection and benchmarking framework for Graph Neural Network (GNN) based fake news detection models, implemented using PyTorch Geometric.
  • mediumtopics#2
    Add 'gnn-framework' to repository topics

    Why:

    CURRENT
    benchmarking, deep-learning, fakenewsdetection, graphneuralnetwork, machine-learning, misinformation, social-media, social-network-analysis
    COPY-PASTE FIX
    benchmarking, deep-learning, fakenewsdetection, graphneuralnetwork, gnn-framework, machine-learning, misinformation, social-media, social-network-analysis
  • lowcomparison#3
    Add a comparison section to differentiate from generic GNN libraries

    Why:

    COPY-PASTE FIX
    ## Comparison with GNN Libraries (PyG, DGL)
While this repository utilizes PyTorch Geometric for implementation, it is distinct from general GNN libraries like PyG or DGL. GNN-FakeNews provides a specialized collection of GNN models specifically for fake news detection, evaluated under the UPFD framework, offering a ready-to-use benchmark for this specific task rather than a general-purpose GNN development toolkit.

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

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface safe-graph/GNN-FakeNews
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Geometric (PyG)
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Geometric (PyG) · recommended 2×
  2. Deep Graph Library (DGL) · recommended 2×
  3. Spektral · recommended 2×
  4. NetworkX · recommended 1×
  5. BERT · recommended 1×
  • CATEGORY QUERY
    How can I implement graph neural networks for detecting fake news on social platforms?
    you: not recommended
    AI recommended (in order):
    1. NetworkX
    2. PyTorch Geometric (PyG)
    3. Deep Graph Library (DGL)
    4. BERT
    5. RoBERTa
    6. XLNet
    7. Hugging Face Transformers
    8. scikit-learn
    9. spaCy
    10. NLTK
    11. Spektral
    12. PyTorch
    13. TensorFlow
    14. TensorBoard
    15. Weights & Biases (W&B)

    AI recommended 15 alternatives but never named safe-graph/GNN-FakeNews. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to benchmark different GNN models for misinformation detection.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric (PyG)
    2. Deep Graph Library (DGL)
    3. Spektral
    4. Graph Neural Network Library (GNNA)
    5. GraphGym

    AI recommended 5 alternatives but never named safe-graph/GNN-FakeNews. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of safe-graph/GNN-FakeNews?
    pass
    AI did not name safe-graph/GNN-FakeNews — 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/GNN-FakeNews in production, what risks or prerequisites should they evaluate first?
    pass
    AI named safe-graph/GNN-FakeNews 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/GNN-FakeNews solve, and who is the primary audience?
    pass
    AI did not name safe-graph/GNN-FakeNews — 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?

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safe-graph/GNN-FakeNews — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite