The global spread of Ebola is due to the complex interactions between individuals, societies, and transportation and trade networks. Understanding and building appropriate statistical and mathematical models of these interactions is vital to responding to the challenges of living in a networked world. There are, of course, many other examples of complex networks — from national power grids and airline networks to social networks, neuronal networks and protein-protein interactions.
Four example complex networks, from top-left to bottom-right: (a) a wiring diagram of the nematode worm brain; (b) a complex network constructed from a chaotic circuit; (c) a partial representation of the author’s Facebook friend network, colored according clustering; and (d) a (fragmented) network of potential infection pathways for avian influenza. Despite the apparently diverse structure and origin, these four structures can be modelled and described by the same theory. In this paper we develop that theory to better understand which of the features of these networks are important, and which may be due to random fluctuation.
Credit: ©Science China Press
Michael Small, Lvlin Hou, and Linjun Zhang
National Science Review, 2014, 1(3): 357-367 doi: 10.1093/nsr/nwu021
Science China Press