Distributed Saddle-Point Dynamics in Multilayer Networks
Multilayer networks provide a more advanced and comprehensive framework for modeling real-world systems compared to traditional single-layer and multiplex networks. Unlike single-layer models, multilayer networks have multiple interacting layers, each with unique topological features. In this paper, we generalize previously developed results for distributed optimization in multiplex networks to the more general case of multilayer networks by employing a tensor formalism to represent multilayer networks and their tensor-Laplacian diffusion dynamics. Although multiplex networks are a special case of multilayer networks, where each layer has the same number of replica nodes connected one-to-one, this generalized framework removes the need for replica nodes, allowing variability in both topology and number of nodes across layers. This approach provides a fully generalized structure for distributed optimization in multilayer networks and enables more complex interlayer connections. We derive the multilayer combinatorial Laplacian tensor and extend the distributed gradient descent algorithm. We provide a theoretical analysis of the convergence of algorithms. Numerical examples validate our approach, and we explore the impact of heterogeneous layer topologies and complex interlayer dynamics on consensus time, underscoring their implications for real-world multilayer systems.