kishormorol

kishormorol / CiteLens

Public

Discover the citing papers that matter most — ranked by impact, relevance, and influence

kishormorol.github.ioCiteLens academic academic-search arxiv bibliometrics citation-analysis
11
2
100% credibility
Found Apr 21, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

CiteLens is an open-source web application that analyzes and ranks papers citing a given research paper using multi-dimensional scores from public scholarly databases.

How It Works

1
🔍 Discover CiteLens

You hear about a helpful tool for finding the most important follow-up papers to any research paper and visit the free online demo.

2
📄 Paste a paper link

Simply paste an arXiv ID, DOI, title, or link into the search box and click Analyze to instantly get a smart ranked list of citing papers.

3
⭐ See ranked results

View the top citing papers sorted by a smart score that considers real impact, relevance to your paper, connections to others, and context.

4
🔧 Filter and explore

Adjust filters for years or relevance, switch to timeline or network graph views to dig deeper into the citation landscape.

5
🎨 Customize your view

Tweak the layout, colors, theme, or density to make the results feel just right for your workflow.

✅ Master your literature

Quickly identify the papers that truly matter, saving hours of manual searching and reading irrelevant citations.

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Star Growth

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AI-Generated Review

What is CiteLens?

CiteLens takes a paper identifier like an arXiv ID, DOI, title, or Semantic Scholar URL and discovers citing papers ranked by a composite score of impact, relevance, network centrality, and context signals. You get explainable results with plain-English breakdowns, smart filters for years or influential-only, timeline charts, and a force-directed network graph where nodes radiate from the seed sized by citations and colored by score tiers. Powered by TypeScript React frontend and Python FastAPI backend using free Semantic Scholar and OpenAlex APIs, it runs a live demo with no signup.

Why is it gaining traction?

It stands out by weighting scores transparently—45% impact via FWCI percentiles, 25% local PageRank—unlike citation-count sorting in Google Scholar or paywalled tools like scite.ai. The interactive graph lets you drag nodes to explore influence clusters, and one-click deploys to Railway mean instant personal instances for discovering repos citing key papers. Zero-config mock data ensures it works offline, hooking devs evaluating GitHub projects by real citation impact.

Who should use this?

Researchers surveying follow-ups to arXiv preprints, lit review writers filtering high-relevance citations, or ML engineers discovering influential papers behind GitHub repos. Ideal for academics needing ranked insights into what citing works matter most by influence, or OSINT analysts tracing paper networks without bloated UIs.

Verdict

With 11 stars and 1.0% credibility, it's immature but battle-tested via pytest suite and live demo—fork for custom ranking if chasing citation discovery tools on GitHub. Solid start; star it to boost visibility.

(198 words)

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