Deep Learning Super-Diffusion in Multiplex Networks

Understand

Complex network theory has shown success in understanding the emergent and collective behavior of complex systems [1].

• Many real-world complex systems were recently discovered to be more accurately modeled as multiplex networks [2-6]---in which each interaction type is mapped to its own network layer; e.g.~multi-layer transportation networks, coupled social networks, metabolic and regulatory networks, etc.
• A salient physical phenomena emerging from multiplexity is super-diffusion: exhibited by an accelerated diffusion admitted by the multi-layer structure as compared to any single layer.
• Theoretically super-diffusion was only known to be predicted using the spectral gap of the full Laplacian of a multiplex network and its interacting layers.