RRepoGEO

REPOGEO REPORT · LITE

Sunefei/PatchNet

Default branch main · commit 0be6003c · scanned 6/8/2026, 3:13:13 AM

GitHub: 509 stars · 20 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Sunefei/PatchNet, 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 README's opening to clearly state the problem and domain

    Why:

    CURRENT
    # PatchNet
Implementation of "Handling Feature Heterogeneity with Learnable Graph Patches" which is accepted by KDD' 25.
    COPY-PASTE FIX
    # PatchNet: Learnable Graph Patches for Heterogeneous Graph Neural Networks
This repository provides the official PyTorch implementation of 'Handling Feature Heterogeneity with Learnable Graph Patches', accepted by KDD'25. PatchNet introduces a novel Graph Neural Network (GNN) approach to effectively manage diverse feature types within heterogeneous graph data by learning and applying dynamic patches.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    graph-neural-networks, gnn, heterogeneous-graphs, feature-learning, deep-learning, kdd2025, pytorch
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. Choose an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and paste its full text into this file.

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 Sunefei/PatchNet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
numpy/numpy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. numpy/numpy · recommended 1×
  2. pandas-dev/pandas · recommended 1×
  3. scikit-learn/scikit-learn · recommended 1×
  4. pyg-team/pytorch_geometric · recommended 1×
  5. dmlc/dgl · recommended 1×
  • CATEGORY QUERY
    How to effectively manage diverse feature types when training graph neural networks?
    you: not recommended
    AI recommended (in order):
    1. NumPy (numpy/numpy)
    2. Pandas (pandas-dev/pandas)
    3. Scikit-learn (scikit-learn/scikit-learn)
    4. PyTorch Geometric (PyG) (pyg-team/pytorch_geometric)
    5. Deep Graph Library (DGL) (dmlc/dgl)
    6. TensorFlow GNN (TF-GNN) (tensorflow/gnn)
    7. Spektral (danielegrattarola/spektral)

    AI recommended 7 alternatives but never named Sunefei/PatchNet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are current approaches for learning robust graph representations from heterogeneous node features?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric (PyG)
    2. Deep Graph Library (DGL)
    3. GCN
    4. GAT
    5. PyTorch
    6. TensorFlow

    AI recommended 6 alternatives but never named Sunefei/PatchNet. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 Sunefei/PatchNet?
    pass
    AI named Sunefei/PatchNet explicitly

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

  • If a team adopts Sunefei/PatchNet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Sunefei/PatchNet 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 Sunefei/PatchNet solve, and who is the primary audience?
    pass
    AI named Sunefei/PatchNet 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 Sunefei/PatchNet. 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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Sunefei/PatchNet — 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