RRepoGEO

REPOGEO REPORT · LITE

jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress

Default branch master · commit 96fb3368 · scanned 6/12/2026, 3:42:36 AM

GitHub: 767 stars · 105 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress, 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

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

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to clarify usage terms

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. For a curated list of papers, a Creative Commons license like CC-BY-4.0 (Attribution 4.0 International) is often suitable, allowing others to share and adapt the content with attribution. Alternatively, if the content is intended to be public domain, consider CC0.
  • mediumhomepage#2
    Add a homepage URL to the repository About section

    Why:

    COPY-PASTE FIX
    Add a relevant URL to the 'Homepage' field in the repository settings. This could be a project website, a related publication, or a personal academic page if applicable.

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 jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. arXiv Sanity Preserver · recommended 1×
  3. Papers With Code · recommended 1×
  4. Twitter · recommended 1×
  5. NeurIPS · recommended 1×
  • CATEGORY QUERY
    Where can I find essential readings to understand the basics of graph neural networks?
    you: not recommended
    Show full AI answer
  • CATEGORY QUERY
    How can I keep up with the latest research and developments in graph neural networks?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. arXiv Sanity Preserver
    3. Papers With Code
    4. Twitter
    5. NeurIPS
    6. ICML
    7. ICLR
    8. KDD
    9. AAAI
    10. Graph Representation Learning and Beyond (GRL+)
    11. Deep Learning on Graphs (DLG)
    12. GitHub
    13. PyTorch Geometric (PyG) (pyg-team/pytorch_geometric)
    14. Deep Graph Library (DGL) (dglai/dgl)
    15. Spektral (danielegrattarola/spektral)
    16. Google Scholar Alerts

    AI recommended 16 alternatives but never named jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress?
    pass
    AI did not name jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress — 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 jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress 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 jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress solve, and who is the primary audience?
    pass
    AI did not name jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress — 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?

Embed your GEO score

Drop this badge into the README of jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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  • Brand-free category queries5 vs 2 in Lite
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