REPOGEO REPORT · LITE

thunlp/OpenNE

Default branch master · commit 7b86f4ca · scanned 6/22/2026, 7:28:06 PM

GitHub: 1,706 stars · 481 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)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 thunlp/OpenNE, 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

Emphasize OpenNE's role as a unified network embedding toolkit in the README

Why:

CURRENT

OpenNE is a sub-project of OpenSKL, providing an **Opensource **N**etwork **E**mbedding toolkit for network representation learning (NRL), with TADW as key features to incorporate text attributes of nodes.

COPY-PASTE FIX

OpenNE is an **Opensource Network Embedding (NE) toolkit** designed for **network representation learning (NRL)**. It provides a **unified, GPU-accelerated framework** for training and evaluating a wide range of NE models, including those that incorporate **text attributes of nodes** like TADW. This makes OpenNE ideal for researchers and practitioners seeking to benchmark and apply diverse NE algorithms.

mediumabout#2
Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://github.com/thunlp/OpenNE
```

lowtopics#3

Expand repository topics to include related concepts and algorithms

Why:

CURRENT

["network-embedding"]

COPY-PASTE FIX

["network-embedding", "node-embedding", "graph-embedding", "representation-learning", "graph-representation-learning", "deepwalk", "node2vec", "text-attributes"]

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 thunlp/OpenNE

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

PyTorch Geometric (PyG)

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

PyTorch Geometric (PyG) · recommended 2×
Deep Graph Library (DGL) · recommended 2×
Spektral · recommended 1×
Graph Neural Network Library (GNNA) · recommended 1×
StellarGraph · recommended 1×

CATEGORY QUERY
What open-source toolkit can I use for network representation learning with GPU support?
you: not recommended
AI recommended (in order):
1. PyTorch Geometric (PyG)
2. Deep Graph Library (DGL)
3. Spektral
4. Graph Neural Network Library (GNNA)
5. StellarGraph
AI recommended 5 alternatives but never named thunlp/OpenNE. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to learn network embeddings that incorporate text attributes of nodes?
you: not recommended
AI recommended (in order):
1. GraphSAGE
2. BERT
3. sentence-transformers
4. Word2Vec
5. Doc2Vec
6. gensim
7. PyTorch Geometric (PyG)
8. Deep Graph Library (DGL)
9. Graph Convolutional Network (GCN)
10. Heterogeneous Graph Attention Networks (HAN)
11. Node2Vec
12. DeepWalk
13. node2vec
14. karateclub
15. Graph Attention Networks (GAT)
AI recommended 15 alternatives but never named thunlp/OpenNE. 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 thunlp/OpenNE?
pass
AI named thunlp/OpenNE explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts thunlp/OpenNE in production, what risks or prerequisites should they evaluate first?
pass
AI named thunlp/OpenNE 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 thunlp/OpenNE solve, and who is the primary audience?
pass
AI named thunlp/OpenNE explicitly
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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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/thunlp/OpenNE"><img src="https://repogeo.com/badge/thunlp/OpenNE.svg" alt="RepoGEO" /></a>

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thunlp/OpenNE — 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