pubnetwork 0.1.3

Publication network analyser - co-author graphs, citation trends, topic clusters from Google Scholar

PubNet

Visualise your research footprint. PubNet takes a Google Scholar profile and turns it into interactive visualisations - co-author networks, citation trends, topic clusters, journal impact factors, and formatted references - all from a single command.

Try the live demo - no install required.

What PubNet does

Paste any Google Scholar profile URL, and PubNet will:

• Map your co-author network as an interactive force-directed graph
• Chart your citation trends over time with a rolling h-index overlay
• Track your publications per year to show research output
• Cluster your work into research topics using TF-IDF and k-means
• Look up journal impact factors from a bundled database of 4,600+ journals
• Correct venue names via the Crossref API before impact factor lookup
• Format every publication as a copyable reference in APA, MLA, BibTeX, Vancouver, or Chicago style
• Export everything as JSON or CSV

PubNet has two interfaces: a CLI that generates self-contained HTML reports you can share, and an interactive web GUI for live exploration with filters, sorting, and cross-linked charts.

Quick start

Option 1: Try it online

Visit pubnetwork.onrender.com and click Analyze. A demo profile loads immediately. To analyse your own profile, paste your Google Scholar URL and click Analyze.

Option 2: Install locally

pip install pubnetwork

Requires Python 3.10+. Then:

# Generate an HTML report
pubnet analyze --scholar-url "https://scholar.google.com/citations?user=ML7X29AAAAAJ"

# Or launch the interactive GUI
pubnet gui

# Or try the bundled demo
pubnet demo

Option 3: Clone and develop

git clone https://github.com/sanjiv856/pubnet.git
cd pubnet
pip install -e ".[dev]"
pytest tests/

CLI reference

pubnet analyze [OPTIONS]

  --scholar-url TEXT           Google Scholar profile URL
  --author-id TEXT             Google Scholar author ID
  --builtin                    Use bundled demo profile
  --format [apa|mla|bibtex|vancouver|chicago]
                               Reference format (default: apa)
  --topics INTEGER             Number of topic clusters (default: 5)
  -o, --output PATH            Output HTML file path
  --no-cache                   Force fresh Scholar fetch
  --crossref / --no-crossref   Crossref venue correction (default: enabled)
  -v, --verbose                Debug logging

pubnet gui                     Launch interactive web GUI at localhost:8050
pubnet demo                    Generate report from bundled demo profile
pubnet cache list              Show cached profiles
pubnet cache clear             Remove all cached data

Profiles are cached to ~/.pubnet/cache/ so repeated analyses don't re-fetch from Scholar.

How it works

Scholar URL → Fetch → Clean/Dedup → Crossref Enrich → Analyse → Render

The core library is a set of pure-function modules shared by both CLI and GUI:

Module	What it does
fetch.py	Fetches Scholar profiles via SerpAPI or scholarly, with JSON caching
analyze.py	Builds co-author graphs, citation trends, topic clusters, summary stats
formatters.py	Formats references in APA, MLA, BibTeX, Vancouver, and Chicago styles
journal_if.py	Looks up impact factors from a bundled 4,600-journal Scimago database, with OpenAlex API fallback
crossref.py	Corrects venue names and retrieves DOIs via the free Crossref REST API
report.py	Renders self-contained HTML reports with embedded Plotly charts
gui/	Dash web app with sidebar navigation, filters, and interactive cross-linked charts

Tech stack

Python 3.10+ with scholarly, pydantic, networkx, dash, dash-cytoscape, plotly, scikit-learn, click, jinja2, rapidfuzz, and SerpAPI for cloud deployment.

Tests

pytest tests/

142+ tests covering models, fetcher, analysis, formatters, report rendering, CLI, and GUI components.

License

MIT. See LICENSE for details.