Abstract We empirically study internal cluster validity indices for attributed networks by evaluating feature- and network-space criteria under controlled generators that decouple attributes from topology while sharing cluster cardinalities. Using unified notation, Gaussian blobs for features, and a stochastic block model for graphs, we assess Silhouette Width (SW), Calinski–Harabasz (CH), Davies–Bouldin (DBI), {S{₃₁ₖ}}, Average Isolability (AVI), Average Unifiability (AVU), and ANUI on both ground-truth and random partitions. SW is stable and saturates once enough features are present; CH grows strongly with sample size (suggesting reporting CH/N) ; DBI and {S{₃₁ₖ}} separate ground truth from random partitions but have K -dependent random baselines, motivating baseline normalization. In network space, AVI increases with assortativity and decreases roughly as 1/ K, AVU drops with K toward a floor, and ANUI follows these trends; all indices approach random baselines as overlap/mixing increases, while confidence intervals narrow with more samples or informative features. We provide an empirical benchmark, simple scaling heuristics, and practical guidance for applying CVIs in attributed networks.
Shalileh et al. (Mon,) studied this question.