Clustering scientific publications: lessons learned through experiments with a real citation network
This work addresses practical challenges in clustering large-scale bibliometric data for researchers and analysts, though it is incremental as it focuses on tuning existing methods rather than introducing new ones.
The study evaluated graph-based clustering algorithms on a real citation network of 700,000 papers and 4.6 million citations, finding that scalable methods like Louvain and Leiden perform efficiently but require careful parameter tuning to avoid poor partitioning, especially in networks with uneven structures.
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.