RRepoGEO

REPOGEO REPORT · LITE

Jiakui/awesome-gcn

Default branch master · commit 7a3e6f57 · scanned 6/2/2026, 11:22:35 PM

GitHub: 914 stars · 134 forks

AI VISIBILITY SCORE
28 /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
2 / 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 Jiakui/awesome-gcn, 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 explicitly state 'awesome list'

    Why:

    CURRENT
    # This repository is to collect GCN, GAT(graph attention) related resources.
    COPY-PASTE FIX
    # Awesome GCN: A Curated List of Graph Convolutional and Attention Network Resources
  • mediumtopics#2
    Add 'awesome-list' and 'resources' to repository topics

    Why:

    CURRENT
    cv, gcn, graph-attention, graph-attention-networks, graph-neural-networks, nlp
    COPY-PASTE FIX
    cv, gcn, graph-attention, graph-attention-networks, graph-neural-networks, nlp, awesome-list, resources, curated-list
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT, Apache-2.0, GPL-3.0) to the repository root.

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 Jiakui/awesome-gcn
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Geometric (PyG) · recommended 1×
  2. Deep Graph Library (DGL) · recommended 1×
  3. Spektral · recommended 1×
  4. tkipf/gcn · recommended 1×
  5. Geometric Deep Learning (Book and Website) · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources and implementations for graph convolutional and attention networks?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric (PyG)
    2. Deep Graph Library (DGL)
    3. Spektral
    4. Graph Neural Networks in PyTorch (by Thomas Kipf) (tkipf/gcn)
    5. Geometric Deep Learning (Book and Website)
    6. Awesome-GNN (awesome-gnn/awesome-gnn)
    7. Google's Graph Neural Network Library (tf_geometric) (tf_geometric/tf_geometric)

    AI recommended 7 alternatives but never named Jiakui/awesome-gcn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good implementations of graph neural networks for computer vision or NLP tasks using Keras?
    you: not recommended
    AI recommended (in order):
    1. Keras-Geometric (danielegrattarola/keras-geometric)
    2. Spektral (danielegrattarola/spektral)
    3. TensorFlow GNN (TF-GNN) (tensorflow/gnn)
    4. Deep Graph Library (DGL) (dmlc/dgl)
    5. Graph Neural Network Library (GNN-LIB)

    AI recommended 5 alternatives but never named Jiakui/awesome-gcn. 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 Jiakui/awesome-gcn?
    pass
    AI named Jiakui/awesome-gcn explicitly

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

  • If a team adopts Jiakui/awesome-gcn in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Jiakui/awesome-gcn 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 Jiakui/awesome-gcn solve, and who is the primary audience?
    pass
    AI did not name Jiakui/awesome-gcn — 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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  • Brand-free category queries5 vs 2 in Lite
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