  This paper focuses on jointly inferring network and community structures from
the dynamics of complex systems. Although many approaches have been designed to
solve these two problems solely, none of them consider explicit shareable
knowledge across these two tasks. Community detection (CD) from dynamics and
network reconstruction (NR) from dynamics are natural synergistic tasks that
motivate the proposed evolutionary multitasking NR and CD framework, called
network collaborator (NC). In the process of NC, the NR task explicitly
transfers several better network structures for the CD task, and the CD task
explicitly transfers a better community structure to assist the NR task.
Moreover, to transfer knowledge from the NR task to the CD task, NC models the
study of CD from dynamics to find communities in the dynamic network and then
considers whether to transfer knowledge across tasks. A test suite for
multitasking NR and CD problems (MTNRCDPs) is designed to verify the
performance of NC. The experimental results conducted on the designed MTNRCDPs
have demonstrated that joint NR with CD has a synergistic effect, where the
network structure used to inform the existence of communities is also
inherently employed to improve the reconstruction accuracy, which, in turn, can
better demonstrate the discovering of the community structure. The code is
available at: https://github.com/xiaofangxd/EMTNRCD.
