Abstract The propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic–awareness, and epidemic–resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

中文翻译：

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复杂网络中的协同进化传播

摘要 现实系统中疾病、行为和信息的传播很少相互独立，而是在强相互作用下共同演化。为了揭示动力学机制，网络协同进化传播的演变时空模式和临界现象极其重要，它们为我们控制流行病传播、预测社会系统中的集体行为等提供了理论基础。因此，复杂网络中的协同进化传播动力学引起了许多学科的广泛关注。在这篇综述中，我们介绍了协同进化传播动力学研究的最新进展，强调了统计力学和网络科学的贡献。理论方法、临界现象、相变、相互作用机制，