Network neuroscience is an emerging interdisciplinary field that aims to understand complex brain function and cognition by integrating empirical and computational approaches. The field focuses on mapping, recording, analyzing, and modeling the elements and interactions within neurobiological systems. Two key trends drive this approach: the development of new empirical tools for comprehensive mapping and dynamic recording, and the advancement of theoretical frameworks and computational methods from network science. These advancements open new frontiers in scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across different scales. The review highlights emerging trends in network neuroscience, emphasizing the importance of network mapping and observation, network analysis and modeling, and the integration of data from different scales and domains. It discusses the challenges and opportunities presented by the increasing volume of big data in neuroscience, and the need for advanced analytics and theoretical ideas to unify our understanding of brain structure and function. Key topics covered include the investigation of network dynamics, the use of network models for prediction, the manipulation and control of brain networks, and the crossing of levels of organization to integrate diverse data sets. The review also explores the application of network science tools to bridge different scales and types of data, and the potential for network models to facilitate integration across micro-, meso-, and macro-connectomics. In conclusion, network neuroscience offers a promising framework for unifying the fragmented discipline of neuroscience by providing a common conceptual and analytical approach. The field is expected to contribute significantly to our understanding of the network mechanisms underlying brain structure and function.Network neuroscience is an emerging interdisciplinary field that aims to understand complex brain function and cognition by integrating empirical and computational approaches. The field focuses on mapping, recording, analyzing, and modeling the elements and interactions within neurobiological systems. Two key trends drive this approach: the development of new empirical tools for comprehensive mapping and dynamic recording, and the advancement of theoretical frameworks and computational methods from network science. These advancements open new frontiers in scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across different scales. The review highlights emerging trends in network neuroscience, emphasizing the importance of network mapping and observation, network analysis and modeling, and the integration of data from different scales and domains. It discusses the challenges and opportunities presented by the increasing volume of big data in neuroscience, and the need for advanced analytics and theoretical ideas to unify our understanding of brain structure and function. Key topics covered include the investigation of network dynamics, the use of network models for prediction, the manipulation and control of brain networks, and the crossing of levels of organization to integrate diverse data sets. The review also explores the application of network science tools to bridge different scales and types of data, and the potential for network models to facilitate integration across micro-, meso-, and macro-connectomics. In conclusion, network neuroscience offers a promising framework for unifying the fragmented discipline of neuroscience by providing a common conceptual and analytical approach. The field is expected to contribute significantly to our understanding of the network mechanisms underlying brain structure and function.