# Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives

arXiv: https://arxiv.org/abs/2506.04045 · HTML: https://mlsanity.com/paper/2506.04045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [Enabling Research through the SCIP Optimization Suite 8.0](https://mlsanity.com/paper/2303.07101/) (2023)
- [Citation-based clustering of publications using CitNetExplorer and VOSviewer](https://mlsanity.com/paper/1702.03411/) (2017)
- [A principled methodology for comparing relatedness measures for clustering publications](https://mlsanity.com/paper/1901.06815/) (2019)
- [Research Portfolio Analysis and Topic Prominence](https://mlsanity.com/paper/1709.03453/) (2017)
- [Optimality conditions based on the Fr'echet second-order subdifferential](https://mlsanity.com/paper/2007.14593/) (2020)

## Abstract (arXiv)

Fuzzy clustering, which allows an article to belong to multiple clusters with soft membership degrees, plays a vital role in analyzing publication data. This problem can be formulated as a constrained optimization model, where the goal is to minimize the discrepancy between the similarity observed from data and the similarity derived from a predicted distribution. While this approach benefits from leveraging state-of-the-art optimization algorithms, tailoring them to work with real, massive databases like OpenAlex or Web of Science - containing about 70 million articles and a billion citations - poses significant challenges. We analyze potentials and challenges of the approach from both mathematical and computational perspectives. Among other things, second-order optimality conditions are established, providing new theoretical insights, and practical solution methods are proposed by exploiting the structure of the problem. Specifically, we accelerate the gradient projection method using GPU-based parallel computing to efficiently handle large-scale data.