RRepoGEO

REPOGEO REPORT · LITE

XiaoxinHe/Awesome-Graph-LLM

Default branch main · commit 1c152958 · scanned 5/15/2026, 12:23:47 AM

GitHub: 2,427 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 XiaoxinHe/Awesome-Graph-LLM, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    awesome-list, graph-llm, large-language-models, graph-neural-networks, nlp, research-papers, knowledge-graphs, graph-reasoning, graph-structured-data
  • highreadme#2
    Reposition the README's opening sentence to clarify its nature

    Why:

    CURRENT
    A collection of AWESOME things about **Graph-Related Large Language Models (LLMs)**.
    COPY-PASTE FIX
    This is an AWESOME list of research papers and resources about **Graph-Related Large Language Models (LLMs)**.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    [Add a relevant URL here, e.g., a project page, a related blog post, or the GitHub repo URL itself if no external site exists]

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 XiaoxinHe/Awesome-Graph-LLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neo4j's GraphRAG Framework
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j's GraphRAG Framework · recommended 1×
  2. OpenAI · recommended 1×
  3. LangChain · recommended 1×
  4. Neo4j · recommended 1×
  5. Amazon Neptune · recommended 1×
  • CATEGORY QUERY
    How can I leverage large language models to process and reason with graph-structured data?
    you: not recommended
    Show full AI answer
  • CATEGORY QUERY
    Looking for research and frameworks exploring the intersection of graph neural networks and LLMs.
    you: not recommended
    AI recommended (in order):
    1. Neo4j's GraphRAG Framework
    2. OpenAI
    3. LangChain
    4. Neo4j
    5. Amazon Neptune
    6. ArangoDB
    7. LlamaIndex
    8. PyTorch Geometric (PyG)
    9. Deep Graph Library (DGL)
    10. PyTorch
    11. TensorFlow
    12. OpenKE/PyKEEN

    AI recommended 12 alternatives but never named XiaoxinHe/Awesome-Graph-LLM. 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 XiaoxinHe/Awesome-Graph-LLM?
    pass
    AI did not name XiaoxinHe/Awesome-Graph-LLM — 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 XiaoxinHe/Awesome-Graph-LLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named XiaoxinHe/Awesome-Graph-LLM 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 XiaoxinHe/Awesome-Graph-LLM solve, and who is the primary audience?
    pass
    AI did not name XiaoxinHe/Awesome-Graph-LLM — 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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XiaoxinHe/Awesome-Graph-LLM — 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