paperank

Core package badges:

Quality and tooling:

Project/community:

Docs:

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 (use cases).

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

---

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.

---

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

---

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="your.email@example.com"
  

• 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

---

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,
    output_format="json"  # or "csv"
)

This will:

• Collect the citation neighborhood around the DOI with 2 iterative crawl steps (each step uses 1-hop neighborhoods)
• Compute PapeRank scores
• Save results to a file (<DOI>.json or <DOI>.csv)

---

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).
• alpha: PageRank damping factor (default 0.85).
• 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
)

---

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.

---

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.

---

Example

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

---

Testing

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

python -m unittest discover tests

---

License

MIT License. See LICENSE for details.

---

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}
}

---

Support & Contributions

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

---

Project Homepage

https://github.com/gwr3n/paperank