Multilayer networks are a generalisation of network that may incorporate different types of interactions . This could be different time points in temporal data, measurements in different individuals or under different experimental conditions. Currently many measures and methods from monolayer networks are extended to be applicabile to multilayer networks. Those include measures of centrality , or methods that enable to find mesoscale structure in networks [3,4].
Examples of such mesoscale structure detection methods are stochastic block models and community detection. Both try to find groups of nodes that behave structurally similar in a network. In its most simplistic way you might think of two groups that are densely connected internally but only sparsely between the groups. For example two classes in a high school, there are many friendships in each class but only a small number between the classes. Often we are interested in how such patterns evolve with time. Here, the usage of multilayer community detection methods is fruitful.Mucha et al. analysed the voting pattern in the U.S. Senate . They find that the communities are oriented as the political party organisation. However, the restructuring of the political landscape over time is observable in the multilayered community structure. For example, the 37th Congress during the beginning of the American Civil War brought a major change in the voting patterns. Modern politics is dominated by a strong partition into Democrats and Republicans with third minor group that can be identified as the ‘Southern Democrats’ that had distinguishable voting patterns during the 1960.
Such multilayer community detection methods can be insightful for networks from other disciplines. For example they have been adopted to describe the reconfiguration in the human brain during learning . Hopefully they will be able to give us insight in the structure and function of protein interaction.
 De Domenico, Manlio; Solé-Ribalta, Albert; Cozzo, Emanuele; Kivelä, Mikko; Moreno, Yamir; Porter, Mason A.; Gómez, Sergio; and Arenas, Alex . Mathematical Formulation of Multilayer Networks, Physical Review X, Vol. 3, No. 4: 041022.
 Taylor, Dane; Myers, Sean A.; Clauset, Aaron; Porter, Mason A.; and Mucha, Peter J. . Eigenvector-based Centrality Measures for Temporal Networks
 Tiago P. Peixoto; Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Phys. Rev. E 92, 042807
 Mucha, Peter J.; Richardson, Thomas; Macon, Kevin; Porter, Mason A.; and Onnela, Jukka-Pekka . Community Structure in Time-Dependent, Multiscale, and Multiplex Networks, Science, Vol. 328, No. 5980: 876-878.
 Bassett, Danielle S.; Wymbs, Nicholas F.; Porter, Mason A.; Mucha, Peter J.; Carlson, Jean M.; and Grafton, Scott T. . Dynamic Reconfiguration of Human Brain Networks During Learning, Proceedings of the National Academy of Sciences of the United States of America, Vol. 118, No. 18: 7641-7646.