RRepoGEO

REPOGEO REPORT · LITE

XiaoxinHe/G-Retriever

Default branch main · commit 315b0ff8 · scanned 6/14/2026, 3:48:17 AM

GitHub: 545 stars · 93 forks

AI VISIBILITY SCORE
35 /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
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 XiaoxinHe/G-Retriever, 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:

    COPY-PASTE FIX
    graph-neural-networks, large-language-models, retrieval-augmented-generation, rag, knowledge-graphs, question-answering, nlp, textual-graph-understanding, neurips-2024
  • highreadme#2
    Reframe README's opening sentence to emphasize framework utility

    Why:

    CURRENT
    This repository contains the source code for the paper "G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering".
    COPY-PASTE FIX
    G-Retriever is a flexible, graph-based question-answering framework that integrates Graph Neural Networks (GNNs), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) for textual graph understanding.
  • mediumhomepage#3
    Update homepage to point to official NeurIPS publication

    Why:

    CURRENT
    https://arxiv.org/abs/2402.07630
    COPY-PASTE FIX
    https://openreview.net/forum?id=MPJ3oXtTZl

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/G-Retriever
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GraphRAG
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GraphRAG · recommended 2×
  2. langchain-ai/langchain · recommended 1×
  3. neo4j/neo4j · recommended 1×
  4. Amazon Neptune · recommended 1×
  5. arangodb/arangodb · recommended 1×
  • CATEGORY QUERY
    How to combine graph neural networks and LLMs for textual graph question answering?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Neo4j (neo4j/neo4j)
    3. Amazon Neptune
    4. ArangoDB (arangodb/arangodb)
    5. TigerGraph
    6. LlamaIndex (run-llama/llama_index)
    7. Hugging Face Transformers (huggingface/transformers)
    8. Deep Graph Library (DGL) (dmlc/dgl)
    9. PyTorch Geometric (PyG) (pytorch/geometric)
    10. GraphRAG
    11. Azure OpenAI Service
    12. OpenAI API
    13. Azure Cosmos DB for Apache Gremlin
    14. QAnswer
    15. Blazegraph (blazegraph/database)

    AI recommended 15 alternatives but never named XiaoxinHe/G-Retriever. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for retrieval-augmented generation on knowledge graphs and common sense reasoning.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. GraphRAG
    4. Neo4j
    5. RDFox
    6. TypeDB

    AI recommended 6 alternatives but never named XiaoxinHe/G-Retriever. 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/G-Retriever?
    pass
    AI named XiaoxinHe/G-Retriever explicitly

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

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

    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 XiaoxinHe/G-Retriever. 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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MARKDOWN (README)
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XiaoxinHe/G-Retriever — 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