RRepoGEO

REPOGEO REPORT · LITE

thunlp/OpenNE

Default branch master · commit 7b86f4ca · scanned 5/12/2026, 1:23:09 PM

GitHub: 1,706 stars · 481 forks

AI VISIBILITY SCORE
54 /100
Needs work
Category recall
1 / 2
Avg rank #6.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 thunlp/OpenNE, 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 the README H1 to highlight text features

    Why:

    CURRENT
    # OpenNE (sub-project of OpenSKL)
    COPY-PASTE FIX
    # OpenNE: An Open-Source Toolkit for Network Embedding with Text Attributes
  • mediumtopics#2
    Expand topics to include specific features and use cases

    Why:

    CURRENT
    network-embedding
    COPY-PASTE FIX
    network-embedding, graph-representation-learning, text-features, node-classification, gpu-acceleration
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    [Insert relevant project or documentation URL here]

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
1 / 2
50% of queries surface thunlp/OpenNE
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
RaRe-Technologies/gensim
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. RaRe-Technologies/gensim · recommended 1×
  2. pyg-team/pytorch_geometric · recommended 1×
  3. dmlc/dgl · recommended 1×
  4. danielegrattarola/spektral · recommended 1×
  5. graph-tool/graph-tool · recommended 1×
  • CATEGORY QUERY
    What open-source tools are available for network representation learning, especially with text features?
    you: not recommended
    AI recommended (in order):
    1. Gensim (RaRe-Technologies/gensim)
    2. PyTorch Geometric (pyg-team/pytorch_geometric)
    3. DGL (dmlc/dgl)
    4. Spektral (danielegrattarola/spektral)
    5. Graph-tool (graph-tool/graph-tool)
    6. StellarGraph (stellargraph/stellargraph)

    AI recommended 6 alternatives but never named thunlp/OpenNE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Comparing different network embedding algorithms for node classification tasks on GPUs?
    you: #6
    AI recommended (in order):
    1. PyTorch Geometric (PyG)
    2. Deep Graph Library (DGL)
    3. Spektral
    4. Graph Neural Network Library (GNNA)
    5. StellarGraph
    6. OpenNE ← you
    7. Karate Club
    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 thunlp/OpenNE?
    pass
    AI named thunlp/OpenNE explicitly

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

  • If a team adopts thunlp/OpenNE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named thunlp/OpenNE 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 thunlp/OpenNE solve, and who is the primary audience?
    pass
    AI named thunlp/OpenNE explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite