The paper presents a generalized framework for detecting community structures in time-dependent, multiscale, and multiplex networks. The authors develop a method to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links connecting nodes in one slice to themselves in other slices. This framework allows for the study of community structures across multiple scales, times, and types of links. The authors derive a null model for multislice networks by formulating a stability of communities under Laplacian dynamics, generalizing the modularity quality function. They show that the null model is equivalent to the stability of communities under Laplacian dynamics and recover null models for bipartite, directed, and signed networks. The method is applied to real-world networks, including the Zachary Karate Club network, U.S. Senate voting similarities, and a multiplex network of college students. The results demonstrate that the proposed framework can provide insights into community structures that would be difficult or impossible to obtain without considering multiple network slices simultaneously. The framework is flexible and can handle networks with multiple variations, making it a powerful tool for studying complex systems.The paper presents a generalized framework for detecting community structures in time-dependent, multiscale, and multiplex networks. The authors develop a method to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links connecting nodes in one slice to themselves in other slices. This framework allows for the study of community structures across multiple scales, times, and types of links. The authors derive a null model for multislice networks by formulating a stability of communities under Laplacian dynamics, generalizing the modularity quality function. They show that the null model is equivalent to the stability of communities under Laplacian dynamics and recover null models for bipartite, directed, and signed networks. The method is applied to real-world networks, including the Zachary Karate Club network, U.S. Senate voting similarities, and a multiplex network of college students. The results demonstrate that the proposed framework can provide insights into community structures that would be difficult or impossible to obtain without considering multiple network slices simultaneously. The framework is flexible and can handle networks with multiple variations, making it a powerful tool for studying complex systems.