One particularly useful construct in the context of dynamic and multimodal networks is that of multilayer networks88. Multilayer networks are networks whose nodes may be connected by different types of edges, with each type being encoded in a different layer90. These layers could, for example, represent different time points, subjects, tasks, brain states, ages or imaging modalities. In multilayer networks, nodes in one layer are connected to corresponding nodes in other layers by identity links (a distinct sort of edge), which hard code the non-independence of data obtained from these nodes. Here we show the simplest case in which all nodes and all edges exist in all layers, but multilayer network tools can also be used in cases in which nodes and edges change across layers. We also illustrate the simplest inter-layer connection pattern, with identity links connecting consecutive © layers; however, alternative connection patterns are possible。
在动态和多模式网络方面，一个特别有用的结构是多层网络。[参考译文]多层网络是这样的网络，其节点可以由不同类型的边连接起来，每一种边都被编码在不同的层中。例如，这些层可以代表不同的时间点、受试者、任务、大脑状态、年龄或成像方式。在多层网络中，一层的节点通过身份链接(一种独特的边缘)连接到另一层的对应节点，这种身份链接对从这些节点获得的数据的非独立性进行了硬编码。这里我们展示了所有节点和所有边存在于所有层中的最简单情况，但是多层网络工具也可以用于节点和边跨层变化的情况。
--------摘自《Network neuroscience（2017，nn.4502）》