RRepoGEO

REPOGEO REPORT · LITE

XiaoxinHe/Awesome-Graph-LLM

Default branch main · commit 1c152958 · scanned 6/25/2026, 2:43:15 PM

GitHub: 2,438 stars · 165 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)

3 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 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 the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    awesome-list, graph-llm, large-language-models, graph-neural-networks, nlp, artificial-intelligence, research, machine-learning, datasets, benchmarks
  • highabout#2
    Refine the repository description for clarity and specificity

    Why:

    CURRENT
    A collection of AWESOME things about Graph-Related LLMs.
    COPY-PASTE FIX
    A curated awesome list of research papers, datasets, benchmarks, and tools focused on Graph-Related Large Language Models (LLMs).
  • mediumreadme#3
    Strengthen the README's opening paragraph to emphasize its 'awesome list' nature

    Why:

    CURRENT
    A collection of AWESOME things about **Graph-Related Large Language Models (LLMs)**.
    COPY-PASTE FIX
    A curated and comprehensive **awesome list** of research papers, datasets, benchmarks, and tools specifically focused on the intersection of **Graph Structures and Large Language Models (LLMs)**.

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
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 2×
  2. LangChain · recommended 2×
  3. LlamaIndex · recommended 2×
  4. Amazon Neptune · recommended 2×
  5. AWS Bedrock · recommended 1×
  • CATEGORY QUERY
    How can I integrate large language models with graph databases for advanced reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. LangChain
    3. LlamaIndex
    4. Amazon Neptune
    5. AWS Bedrock
    6. Amazon SageMaker
    7. AuraDB
    8. Google Cloud Vertex AI
    9. ArangoDB
    10. OpenAI API
    11. GraphDB

    AI recommended 11 alternatives but never named XiaoxinHe/Awesome-Graph-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best resources for combining LLMs with graph structures for various applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Neo4j
    4. Neo4j AuraDS
    5. Neo4j GDS Library
    6. GraphRAG
    7. Amazon Neptune
    8. PyTorch-BigGraph
    9. DeepWalk
    10. RDFox

    AI recommended 10 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?

Embed your GEO score

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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