论文标题
基于其动力和演变的加权复合网络的不断发展模型的调查
A Survey of Evolving Models for Weighted Complex Networks based on their Dynamics and Evolution
论文作者
论文摘要
几十年来，复杂的网络（例如社交网络，生物网络，化学网络，技术网络）一直被用来研究各种复杂系统的演变和动态。可以使用加权链接更好地描述这些复杂的系统，因为二进制连接不会描绘系统的完整信息。所有这些加权网络通过遵循不同的潜在力学来发展在不同的环境中。研究人员致力于揭示加权网络不断发展的现象，以了解其结构和动态。在本章中，我们讨论了加权网络和不断发展的模型的演变，以生成不同类型的合成加权网络，包括无向，指示，签名，多层，社区和核心外围结构加权网络。我们进一步讨论了生成的合成网络持有的各种属性及其与现实世界加权网络的相似性。
For decades, complex networks, such as social networks, biological networks, chemical networks, technological networks, have been used to study the evolution and dynamics of different kinds of complex systems. These complex systems can be better described using weighted links as binary connections do not portray the complete information of the system. All these weighted networks evolve in a different environment by following different underlying mechanics. Researchers have worked on unraveling the evolving phenomenon of weighted networks to understand their structure and dynamics. In this chapter, we discuss the evolution of weighted networks and evolving models to generate different types of synthetic weighted networks, including undirected, directed, signed, multilayered, community, and core-periphery structured weighted networks. We further discuss various properties held by generated synthetic networks and their similarity with real-world weighted networks.
