  Network representations have been effectively employed to analyze complex
systems across various areas and applications, leading to the development of
network science as a core tool to study systems with multiple components and
complex interactions. There is a growing interest in understanding the temporal
dynamics of complex networks to decode the underlying dynamic processes through
the temporal changes in network structure. Community detection algorithms,
which are specialized clustering algorithms, have been instrumental in studying
these temporal changes. They work by grouping nodes into communities based on
the structure and intensity of network connections over time aiming to maximize
modularity of the network partition. However, the performance of these
algorithms is highly influenced by the selection of resolution parameters of
the modularity function used, which dictate the scale of the represented
network, both in size of communities and the temporal resolution of dynamic
structure. The selection of these parameters has often been subjective and
heavily reliant on the characteristics of the data used to create the network
structure. Here, we introduce a method to objectively determine the values of
the resolution parameters based on the elements of self-organization. We
propose two key approaches: (1) minimization of the biases in spatial scale
network characterization and (2) maximization of temporal scale-freeness. We
demonstrate the effectiveness of these approaches using benchmark network
structures as well as real-world datasets. To implement our method, we also
provide an automated parameter selection software package that can be applied
to a wide range of complex systems.
