Multiscale dynamical embeddings of complex networks

Complex systems and relational data are often abstracted as dynamical processes on networks. To understand, predict and control their behavior a crucial step is to extract reduced descriptions of such networks. Inspired by notions from Control Theory, here we propose a time-dependent dynamical similarity measure between nodes, which quantifies the effect that a node input has on the network over time. This dynamical similarity induces an embedding that can be employed for several analysis tasks. Here we focus on (i) dimensionality reduction, by projecting nodes onto a low dimensional space capturing dynamic similarity at different time scales, and (ii) how to exploit our embeddings to uncover functional modules. We exemplify our ideas through case studies focusing on directed networks without strong connectivity, and signed networks. We further highlight how several ideas from community detection can be generalized in terms of our embedding perspective and linked to ideas from Control Theory.