paperank 0.2.1

A publication ranking and citation network analysis tools.

paperank

A Publication Ranking and Citation Network Analysis Tools

paperank is a Python package for analyzing scholarly impact using citation networks. It provides tools to build citation graphs from DOIs, compute PapeRank (a PageRank-like score), fetch publication metadata, and export ranked results. The package is designed for researchers, bibliometricians, and developers interested in quantifying publication influence within local or global citation networks.

For a discussion on the use of PageRank-like scores beyond the web see Gleich, 2014.

Use cases.

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Features

• Citation Graph Construction:
  Automatically builds a citation network from a starting DOI, including both cited and citing works, with configurable depth.

• PapeRank Computation:
  Calculates PageRank-like scores for all publications in the network, quantifying their relative importance.

• Metadata Retrieval:
  Fetches publication metadata (authors, title, year, etc.) from Crossref and OpenCitations.

• Export Ranked Results:
  Outputs ranked publication lists to JSON or CSV files, including scores and metadata.

• Robust HTTP Handling:
  Uses retry logic for API requests to handle rate limits and transient errors.

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Installation

Install via pip (recommended):

pip install paperank

Or clone the repository and install locally:

git clone https://github.com/gwr3n/paperank.git
cd paperank
pip install .

Dependencies are managed via pyproject.toml and include:

• numpy
• scipy
• requests
• tqdm
• urllib3

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Requirements and configuration

• Python 3.8+ is recommended.

• Set CROSSREF_MAILTO to help Crossref identify your traffic and improve reliability:

  macOS/Linux (bash/zsh):

  export CROSSREF_MAILTO="[e-mail]"
  

• Progress parameter (used across APIs): one of

  • False: no progress
  • True: basic progress (or fallback)
  • 'tqdm': explicitly request tqdm progress bars
  • int: print every N iterations/steps

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Quick Start

Here’s a minimal example to rank publications in a citation neighborhood:

from paperank import crawl_and_rank_frontier

# Set your target DOI
doi = "10.1016/j.ejor.2005.01.053"

# Run the analysis
results = crawl_and_rank_frontier(
    doi=doi,
    steps=2,
    alpha=0.85,
    output_format="json",  # or "csv"
    debug=False,
    progress=True
)

This will:

• Collect the citation neighborhood around the DOI
• Compute PapeRank scores
• Save results to a file (<DOI>.json or <DOI>.csv)

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Advanced Parameters

You can fine-tune the crawl and ranking via the following parameters:

• min_year: Optional minimum publication year to include during crawling (filters older works).
• min_citations: Optional minimum total citation count to include during crawling (filters low-signal works).
• tol: Convergence tolerance for the power iteration (default 1e-12).
• max_iter: Maximum number of power-iteration steps (default 10000).
• teleport: Optional teleportation distribution (NumPy array of size N), non-negative and summing to 1. If None, a uniform distribution is used.

Example:

from paperank import crawl_and_rank_frontier

results = crawl_and_rank_frontier(
    doi="10.1016/j.ejor.2005.01.053",
    steps=1,
    min_year=2000,       
    min_citations=5,     
    alpha=0.85,
    tol=1e-12,
    max_iter=20000,
    teleport=None,
)

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Main API

• crawl_and_rank_frontier:
  End-to-end workflow for crawling a citation network and ranking publications.

• rank:
  Compute PapeRank scores for a list of DOIs.

• rank_and_save_publications_JSON:
  Save ranked results to a JSON file.

• rank_and_save_publications_CSV:
  Save ranked results to a CSV file.

• crawl_citation_neighborhood:
  Iteratively crawl 1-hop bidirectional neighborhoods and union results.

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Submodules

• citation_crawler:
  Functions for recursive citation/citing DOI collection.

• citation_matrix:
  Builds sparse adjacency matrices for citation graphs.

• paperank_matrix:
  Matrix utilities for stochastic and PageRank computations.

• crossref:
  Metadata retrieval from Crossref.

• open_citations:
  Citing DOI retrieval from OpenCitations.

• doi_utils:
  DOI normalization and utility functions.

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Example

See example.py for a comprehensive script demonstrating the workflow (including advanced parameters).

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Testing

Unit tests are provided in the tests directory. Run with:

python -m unittest discover tests

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License

MIT License. See LICENSE for details.

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Citation

If you use paperank in published work, please cite the repository:

@software{rossi2025paperank,
  author = {Roberto Rossi},
  title = {paperank: a publication ranking and citation network analysis tools},
  year = {2025},
  url = {https://github.com/gwr3n/paperank}
}

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Support & Contributions

• Issues and feature requests: GitHub Issues
• Pull requests welcome!

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Project Homepage

https://github.com/gwr3n/paperank