RRepoGEO

REPOGEO REPORT · LITE

LirongWu/awesome-graph-self-supervised-learning

Default branch main · commit 16e4a203 · scanned 5/28/2026, 2:48:26 AM

GitHub: 1,435 stars · 165 forks

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 LirongWu/awesome-graph-self-supervised-learning, 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
  • highabout#1
    Clarify repository description to reflect 'awesome list' nature

    Why:

    CURRENT
    Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"
    COPY-PASTE FIX
    A curated list of resources for self-supervised learning on graphs, covering contrastive, generative, and predictive methods.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root to clearly state the terms of use for the curated list and any associated content.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the URL of the associated TKDE paper or a project page (if one exists) to the repository's homepage field.

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 LirongWu/awesome-graph-self-supervised-learning
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Geometric (PyG) · recommended 1×
  2. Deep Graph Library (DGL) · recommended 1×
  3. Spektral · recommended 1×
  4. GraphVPR (Graph Contrastive Learning Toolkit) · recommended 1×
  5. Open Graph Benchmark (OGB) · recommended 1×
  • CATEGORY QUERY
    How to learn effective graph representations using self-supervised methods for downstream tasks?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric (PyG)
    2. Deep Graph Library (DGL)
    3. Spektral
    4. GraphVPR (Graph Contrastive Learning Toolkit)
    5. Open Graph Benchmark (OGB)
    6. GraphGym

    AI recommended 6 alternatives but never named LirongWu/awesome-graph-self-supervised-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best techniques for pre-training graph neural networks using unsupervised learning?
    you: not recommended
    AI recommended (in order):
    1. Deep Graph Infomax (DGI)
    2. GraphCL
    3. GRACE
    4. BGRL
    5. CCA-SSG
    6. GraphMAE
    7. GraphBERT
    8. GraphRNN
    9. NetGAN
    10. GraphVAE
    11. Node2Vec
    12. DeepWalk
    13. LINE

    AI recommended 13 alternatives but never named LirongWu/awesome-graph-self-supervised-learning. 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 LirongWu/awesome-graph-self-supervised-learning?
    pass
    AI did not name LirongWu/awesome-graph-self-supervised-learning — 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 LirongWu/awesome-graph-self-supervised-learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LirongWu/awesome-graph-self-supervised-learning 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 LirongWu/awesome-graph-self-supervised-learning solve, and who is the primary audience?
    pass
    AI did not name LirongWu/awesome-graph-self-supervised-learning — 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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
LirongWu/awesome-graph-self-supervised-learning — RepoGEO report