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。

在動態和多模式網路方面，一個特別有用的結構是多層網路。[參考譯文]多層網路是這樣的網路，其節點可以由不同型別的邊連線起來，每一種邊都被編碼在不同的層中。例如，這些層可以代表不同的時間點、受試者、任務、大腦狀態、年齡或成像方式。在多層網路中，一層的節點通過身份連結(一種獨特的邊緣)連線到另一層的對應節點，這種身份連結對從這些節點獲得的資料的非獨立性進行了硬編碼。這裡我們展示了所有節點和所有邊存在於所有層中的最簡單情況，但是多層網路工具也可以用於節點和邊跨層變化的情況。

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