Similarity-Weighted Leiden-CPM Clustering of a 7.4-Million-Node Biomedical Citation Graph: Weight Alignment, Scale, and Sparsity

Abstract

Clustering citation networks into research communities supports science mapping, research evaluation, and the analysis of funding portfolios. Biomedical articles also carry metadata, such as MeSH terms, publication year, and abstracts, that can be turned into similarity-based edge weights for Leiden clustering under the Constant Potts Model (CPM). Studies of hybrid relatedness compare the accuracy of such clusterings, but how the weights interact with the CPM resolution parameter on a large graph is less clear. Here it is shown that, on a citation network derived from the cell biology research literature (7.4 million nodes, 57.5 million edges), the effect of the weights is governed mainly by their scale and by edges of weight zero, and that, once these are controlled, weighting gives modestly more coherent clusters. Edges were weighted with seven topological and semantic similarity features and clustered with Leiden-CPM, using a method updated to fix a bug in how earlier code attached weights to edges. Mean weights of 0.0015 to 0.23 raised the effective resolution 4- to 650-fold; dividing by the mean restored most of the coverage, except for topological weightings, in which the 47% of edges with weight zero hold most of it. At equal coverage, weighted clusters were more coherent than unweighted ones, also on features outside the weight. For weighted CPM clustering, weights should therefore be attached to edges by key, normalized or reported with their effective resolution, and checked for zero-weight edges.