Topological dimension tunes activity patterns in hierarchical modular network models (1705.08145v1)

Abstract: Connectivity patterns of relevance in neuroscience and systems biology can be encoded in hierarchical modular networks (HMNs). Moreover, recent studies highlight the role of hierarchical modular organization in shaping brain activity patterns, providing an excellent substrate to promote both the segregation and integration of neural information. Here we propose an extensive numerical analysis of the critical spreading rate (or "epidemic" threshold) --separating a phase with endemic persistent activity from one in which activity ceases-- on diverse HMNs. By employing analytical and computational techniques we determine the nature of such a threshold and scrutinize how it depends on general structural features of the underlying HMN. We critically discuss the extent to which current graph-spectral methods can be applied to predict the onset of spreading in HMNs, and we propose the network topological dimension as a relevant and unifying structural parameter, controlling the epidemic threshold.