RRepoGEO

REPOGEO REPORT · LITE

BUPT-GAMMA/OpenHGNN

Default branch main · commit f7ffec18 · scanned 6/8/2026, 1:56:53 PM

GitHub: 979 stars · 166 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 BUPT-GAMMA/OpenHGNN, 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
    Clarify OpenHGNN's role as a benchmarking toolkit in the README's opening

    Why:

    CURRENT
    This is an open-source toolkit for Heterogeneous Graph Neural Network based on DGL and PyTorch. We integrate SOTA models of heterogeneous graph.
    COPY-PASTE FIX
    OpenHGNN is a unified, open-source toolkit for Heterogeneous Graph Neural Network (HGNN) research, providing a comprehensive library for benchmarking and fair comparison of SOTA models based on DGL and PyTorch.
  • hightopics#2
    Add specific topics to improve category recall for HGNN benchmarking

    Why:

    CURRENT
    dgl, graph-neural-networks, heterogeneous, pytorch
    COPY-PASTE FIX
    dgl, graph-neural-networks, heterogeneous, pytorch, hgnn, benchmarking, research-framework
  • mediumhomepage#3
    Add the official documentation URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://openhgnn.readthedocs.io/en/latest/

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 BUPT-GAMMA/OpenHGNN
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Geometric (PyG) · recommended 2×
  2. Deep Graph Library (DGL) · recommended 2×
  3. Spektral · recommended 2×
  4. Graph Neural Network Library (GNN-LIB) · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    What are the best toolkits for building heterogeneous graph neural networks with PyTorch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric (PyG)
    2. Deep Graph Library (DGL)
    3. Spektral
    4. Graph Neural Network Library (GNN-LIB)
    5. PyTorch
    6. torch_sparse

    AI recommended 6 alternatives but never named BUPT-GAMMA/OpenHGNN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to develop heterogeneous graph neural networks for recommendation tasks.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric (PyG)
    2. Deep Graph Library (DGL)
    3. Spektral
    4. Graph Neural Network Library (GNN-LIB) (from Alibaba)
    5. StellarGraph
    6. Neo4j GDS (Graph Data Science Library)

    AI recommended 6 alternatives but never named BUPT-GAMMA/OpenHGNN. 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 BUPT-GAMMA/OpenHGNN?
    pass
    AI named BUPT-GAMMA/OpenHGNN explicitly

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

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

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

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BUPT-GAMMA/OpenHGNN — 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