This paper presents a generalized framework for detecting community structure in time-dependent, multiscale, and multiplex networks. The authors developed a method to study community structure in multislice networks, which are combinations of individual networks connected through links that connect each node in one slice to itself in other slices. This framework allows for the study of networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales. The study of network communities is important in network science, as communities are defined as groups of nodes that are more tightly connected to each other than to the rest of the network. The authors developed a quality function to quantify communities, which counts intra-community edges compared to what would be expected at random. They also considered the choice of null model, which is crucial in studying network community structure. The authors generalized this approach to multislice networks, allowing for the study of networks with multiple types of links, multiple scales, and time-dependent structures. The authors developed a methodology to remove the limitations of previous approaches, generalizing the determination of community structure via quality functions to multislice networks. They derived a principled generalization of community detection to multislice networks, with a single parameter controlling the inter-slice correspondence of communities. They also generalized the modularity quality function to include different types of networks, such as bipartite, directed, and signed networks. The authors applied these generalizations to derive null models for multislice networks that extend the existing quality-function methodology, including an additional parameter to control the coupling between slices. They demonstrated the effectiveness of their method using examples such as the Zachary Karate Club network, the U.S. Senate roll call voting data, and a multiplex network of college students. These examples showed that multislice community detection can reveal insights that would be difficult or impossible to obtain without considering multiple network slices. The authors concluded that their multislice framework allows for the study of community structure in a much broader class of networks than was previously possible. Their method enables the simultaneous study of community structure across multiple times, multiple resolution parameter values, and multiple types of links. This approach has the potential to become a powerful tool for studying complex systems that have multiple features, such as time-dependent multiplex networks.This paper presents a generalized framework for detecting community structure in time-dependent, multiscale, and multiplex networks. The authors developed a method to study community structure in multislice networks, which are combinations of individual networks connected through links that connect each node in one slice to itself in other slices. This framework allows for the study of networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales. The study of network communities is important in network science, as communities are defined as groups of nodes that are more tightly connected to each other than to the rest of the network. The authors developed a quality function to quantify communities, which counts intra-community edges compared to what would be expected at random. They also considered the choice of null model, which is crucial in studying network community structure. The authors generalized this approach to multislice networks, allowing for the study of networks with multiple types of links, multiple scales, and time-dependent structures. The authors developed a methodology to remove the limitations of previous approaches, generalizing the determination of community structure via quality functions to multislice networks. They derived a principled generalization of community detection to multislice networks, with a single parameter controlling the inter-slice correspondence of communities. They also generalized the modularity quality function to include different types of networks, such as bipartite, directed, and signed networks. The authors applied these generalizations to derive null models for multislice networks that extend the existing quality-function methodology, including an additional parameter to control the coupling between slices. They demonstrated the effectiveness of their method using examples such as the Zachary Karate Club network, the U.S. Senate roll call voting data, and a multiplex network of college students. These examples showed that multislice community detection can reveal insights that would be difficult or impossible to obtain without considering multiple network slices. The authors concluded that their multislice framework allows for the study of community structure in a much broader class of networks than was previously possible. Their method enables the simultaneous study of community structure across multiple times, multiple resolution parameter values, and multiple types of links. This approach has the potential to become a powerful tool for studying complex systems that have multiple features, such as time-dependent multiplex networks.