Gang Yan

TL;DR: A simple model with which to grow, in the presence of a given level of inclusivity, networks which represent the structure of organisations, and it is shown that, in comparison to exclusivity, inclusiveness promotes unity, efficiency and robustness.

TL;DR: In this article, through an analysis of 100 million Weibo and 40 million Twitter users, the authors identify percolation-like spread and find that it happens more readily than current theoretical models would predict, with positive feedback in the coevolution between network structure and user activity level, such that more active users gain more followers.

TL;DR: The effects of degree heterogeneity on stability vary with different types of interspecific interactions, while its effects on networks of predator–prey interactions such as food webs depend on prey contiguity, i.e. the extent to which the species consume an unbroken sequence of prey in community niche space.

TL;DR: An entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network, is proposed and it is found that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combinedTopological-temporal predictability is higher than the temporal predictability.

TL;DR: This work model the information traffic on scale-free networks considering the packets’ limited lifetime and finds that the network systems undergo a continuous transition from stable state to explored state at a critical point Rd.