# Clustering scientific publications: lessons learned through experiments with a real citation network

arXiv: https://arxiv.org/abs/2505.18180 · HTML: https://mlsanity.com/paper/2505.18180/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 1 of 1)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)

## Built on by (most cited first, 1 of 1)

- [Reconnecting Fragmented Citation Networks with Semantic Augmentation](https://mlsanity.com/paper/2605.12263/) (2026)

## Abstract (arXiv)

Clustering scientific publications can reveal underlying research structures within bibliographic databases. Graph-based clustering methods, such as spectral, Louvain, and Leiden algorithms, are frequently utilized due to their capacity to effectively model citation networks. However, their performance may degrade when applied to real-world data. This study evaluates the performance of these clustering algorithms on a citation graph comprising approx. 700,000 papers and 4.6 million citations extracted from Web of Science. The results show that while scalable methods like Louvain and Leiden perform efficiently, their default settings often yield poor partitioning. Meaningful outcomes require careful parameter tuning, especially for large networks with uneven structures, including a dense core and loosely connected papers. These findings highlight practical lessons about the challenges of large-scale data, method selection and tuning based on specific structures of bibliometric clustering tasks.