pubnetwork 0.1.0

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

PubNet

Publication network analyser for researchers. Given a Google Scholar profile, PubNet fetches your publications and generates interactive visualisations: co-author networks, citation trends, topic clusters, journal impact factors, and formatted references.

Features

• Co-author network graph - interactive force-directed graph showing collaboration patterns
• Citation trends - yearly citation counts with rolling h-index overlay
• Publications per year - output volume over time
• Topic clusters - TF-IDF + k-means clustering of research themes
• Journal impact factors - Scimago CSV lookup with OpenAlex API fallback
• Crossref enrichment - corrects venue names and adds DOIs via free Crossref API
• Reference formatting - APA, MLA, BibTeX, Vancouver, Chicago with copy-to-clipboard
• Two interfaces - CLI (self-contained HTML report) and Dash GUI (live interactive exploration)

Install

pip install pubnetwork

Or for development:

git clone https://github.com/sanjiv856/pubnet.git
cd pubnet
pip install -e .

Requires Python 3.10+.

Quick start

Demo (bundled profile)

pubnet demo

Generates sanjiv_kumar_pubnet.html using the bundled Scholar profile.

Analyse a Scholar profile

pubnet analyze --scholar-url "https://scholar.google.com/citations?user=ML7X29AAAAAJ"

Or by author ID:

pubnet analyze --author-id ML7X29AAAAAJ

Interactive GUI

pubnet gui

Opens a Dash web app at http://localhost:8050 with sidebar navigation, filters, and interactive charts.

CLI options

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

Architecture

Fetch (scholarly) -> Clean/Dedup (rapidfuzz) -> Crossref Enrich -> Analyse -> Render

Core library with pure-function analysis modules shared by both CLI and GUI:

Module	Purpose
fetch.py	Scholar fetcher with JSON cache
analyze.py	Co-author graph, citation trends, topic clusters, stats
formatters.py	APA/MLA/BibTeX/Vancouver/Chicago references
journal_if.py	Scimago CSV + OpenAlex API impact factors
crossref.py	Free Crossref API for venue correction
report.py	Jinja2 HTML report renderer
gui/	Dash interactive app

Cache management

pubnet cache list    # Show cached profiles
pubnet cache clear   # Remove all cached data

Profiles are cached to ~/.pubnet/cache/ to avoid repeated Scholar fetches.

Tech stack

Python 3.10+ with scholarly, pydantic, networkx, dash, dash-cytoscape, plotly, scikit-learn, click, jinja2, rapidfuzz.

Tests

pip install -e .
pytest tests/