  Dynamic community detection is crucial for elucidating the temporal evolution
of social structures, information dissemination, and interactive behaviors
within complex networks. Nonnegative matrix factorization provides an efficient
framework for identifying communities in static networks but fall short in
depicting temporal variations in community affiliations. To solve this problem,
this paper proposes a Modularity maximization-incorporated Nonnegative Tensor
RESCAL Decomposition (MNTD) model for dynamic community detection. This method
serves two primary functions: a) Nonnegative tensor RESCAL decomposition
extracts latent community structures in different time slots, highlighting the
persistence and transformation of communities; and b) Incorporating an initial
community structure into the modularity maximization algorithm, facilitating
more precise community segmentations. Comparative analysis of real-world
datasets shows that the MNTD is superior to state-of-the-art dynamic community
detection methods in the accuracy of community detection.
