Fig. 1

Overview of the structural similarity analysis and clustering of social network layers. (a) Ten peer-nominated social layers, including two layers related to perceived drinking behavior, were collected from college student clubs. (b) Example illustration of three directed network layers (e.g., Like, Drinking Most, and Good News) representing different social relationships within a single student group. Each layer represents a specific type of social network (e.g., Like, Drinking Most, and Good News) and contains directed links between students (nodes), based on peer nominations. Green arrows indicate directed ties that are shared across multiple layers, and yellow highlights mark local triadic structures. The elements used for node-, edge-, and triad-level analysis are shown. (c) Social layers are positioned along a conceptual axis from hierarchical (e.g., Leaders layer) to horizontal (e.g., Affiliation-related layers), reflecting the degree of directionality and reciprocity in each network. (d) Node-, edge-, and triad-level similarity measures are used to perform unsupervised k-means clustering of network layers. Structural similarity scores are projected onto the first two dimensions (Dim 1 and Dim 2) of a low-dimensional embedding (e.g., PCA), where Dim 1 and Dim 2 capture dominant patterns of structural similarity across layers. Each layer is also positioned along a conceptual hierarchical–horizontal axis. The alignment between these two independent perspectives–an unsupervised clustering and a projection onto a conceptual relationship axis–highlights groupings of social layers that are both structurally and functionally similar.