Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node’s importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network.

Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there’s an increasing focus on the rapid development of more complicated networks, namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on a multilayer network without modifications. Thus, in this paper, we offer overall comparisons of existing works and analyze several representative algorithms, providing a comprehensive understanding of community detection methods in multilayer networks. The comparison results indicate that the promoting of algorithm efficiency and the extending for general multilayer networks are also expected in the forthcoming studies.

A new pipe-routing method for aero-engines is proposed in this article. Careful consideration of the spatial characteristics and the primary engineering constraints of aero-engines yielded a new space representation method as well as a space diving method for the aero-engine surface, which simplify the searching space. A genetic algorithm, along with certain modified strategies, including the ‘initiation’ and ‘direction guideline’, is also developed for pipe-routing in the sub-spaces. Simulation experiments in the context of various scenes have been carried out to explore the applicability and performance of the propose method. Simulation results showed that this novel method can quickly deliver the optimal routes for any aero-engine of large space and with complex mechanical components while avoiding convergence into local optimal value in comparison with some existing published methods.