networks with power law tails in degree distributions, hierarchies, non-trivial clustering and degree mixing patterns. Another step towards a generic framework for networks science is made by Lizier et al. [13], who investigate computational capabilities of small-world networks in terms of information-theoretic measures. The analysis includes topological and dynamical phase transitions, and associates specific modes of computation (such as storage and transfer) with well-known phases of the dy- namics and randomness in topology. The main result is the observation that the information storage and information transfer are somewhat balanced near the small-world regime, providing quantitative evidence that small-world networks are capable of supporting comparably large information storage and transfer capacity. This observation may open a way for explaining, within a general information-theoretic framework, the prevalence of small-world occurrence in naturally occurring networks. On the same path lies the study of the order-chaos phase transition in ran- dom Boolean networks (RBNs), which have been used as models of gene regula- tory networks [11], carried out by Wang et al. [22]. This work characterizes the RBN dynamics via a phase diagram obtained with respect to Fisher informa- tion. This novelty offers a natural interpretation of the phase diagram—through a generic measure capturing information-theoretically how much system dynam- ics can reveal about its control parameters. The observation that this measure is maximized near the order-chaos phase transitions concurs well with the charac- terization of the “edge of chaos” as the region where the system is most sensitive to its parameters. Importantly, the study exemplifies how Fisher information may be used as a powerful generic tool in network science. Another paper that uses RBNs is that of Poblanno-Balp and Gershenson [20], where the effect of a modular topology is studied on the properties and 3