Impact of Intra-Layer Clustering on Information Diffusion Dynamics in Multilayer Complex Networks

Complex networks represent non-trivial topologies of connected natural and artificial systems. Revealing how information spreads within such multilayered systems is critical to the investigation of processes such as social influence, rumor spread, and epidemic spread. This article presents a clustering method based on a centrality-based measure of a multilayer personalized PageRank (m-PageRank) that aggregates node influence across layers to identify structurally and functionally cohesive clusters. The suggested framework supports realistic modeling of intra- and inter-cluster information propagation in complex networks. In addition, we examine the effects of clustering properties on diffusion efficiency and the epidemic threshold. Experimental verification shows that the m-PageRank-based clustering improves diffusion insight by effectively detecting influential nodes and tight communities, thus uncovering how enhanced clustering conditions the collective spread dynamics in multilayer networks.