REPOGEO REPORT · LITE

flyingdoog/awesome-graph-explainability-papers

Default branch main · commit 65ba6d66 · scanned 6/9/2026, 7:48:15 PM

GitHub: 806 stars · 76 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

22 /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

1 / 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 flyingdoog/awesome-graph-explainability-papers, 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 clarify it's a curated list of papers

Why:

CURRENT

# awesome-graph-explainability-papers
Papers about the explainability of GNNs

COPY-PASTE FIX

# awesome-graph-explainability-papers
A curated list of essential papers and surveys on the explainability of Graph Neural Networks (GNNs).

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

(Create a LICENSE file in the repository root, e.g., with an MIT or Apache-2.0 license, or clearly state the intended license in the README if it's a custom one.)

mediumtopics#3

Add 'awesome-list', 'survey-papers', and 'research-papers' to topics

Why:

CURRENT

deep-learning, explainability, explainable-ai, graph-mining, graph-neural-networks, machine-learning, xai

COPY-PASTE FIX

deep-learning, explainability, explainable-ai, graph-mining, graph-neural-networks, machine-learning, xai, awesome-list, survey-papers, research-papers

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 flyingdoog/awesome-graph-explainability-papers

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

GNNExplainer

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

GNNExplainer · recommended 1×
PGExplainer · recommended 1×
Grad-CAM · recommended 1×
Captum · recommended 1×
GraphMask · recommended 1×

CATEGORY QUERY
How can I understand the decision-making process of my graph neural networks?
you: not recommended
AI recommended (in order):
1. GNNExplainer
2. PGExplainer
3. Grad-CAM
4. Captum
5. GraphMask
6. XGNN
7. SHAP
AI recommended 7 alternatives but never named flyingdoog/awesome-graph-explainability-papers. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find recent research and surveys on explaining graph-based deep learning models?
you: not recommended
AI recommended (in order):
1. arXiv.org
2. Google Scholar
3. ACM Digital Library
4. IEEE Xplore
5. OpenReview.net
6. GitHub
AI recommended 6 alternatives but never named flyingdoog/awesome-graph-explainability-papers. 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 flyingdoog/awesome-graph-explainability-papers?
pass
AI did not name flyingdoog/awesome-graph-explainability-papers — 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?
If a team adopts flyingdoog/awesome-graph-explainability-papers in production, what risks or prerequisites should they evaluate first?
pass
AI named flyingdoog/awesome-graph-explainability-papers 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 flyingdoog/awesome-graph-explainability-papers solve, and who is the primary audience?
pass
AI did not name flyingdoog/awesome-graph-explainability-papers — 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?

Embed your GEO score

Drop this badge into the README of flyingdoog/awesome-graph-explainability-papers. 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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HTML

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flyingdoog/awesome-graph-explainability-papers — 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