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This paper focuses on jointly inferring network and community structures fromthe dynamics of complex systems. Although many approaches have been designed tosolve these two problems solely, none of them consider explicit shareableknowledge across these two tasks. Community detection (CD) from dynamics andnetwork reconstruction (NR) from dynamics are natural synergistic tasks thatmotivate the proposed evolutionary multitasking NR and CD framework, callednetwork collaborator (NC). In the process of NC, the NR task explicitlytransfers several better network structures for the CD task, and the CD taskexplicitly transfers a better community structure to assist the NR task.Moreover, to transfer knowledge from the NR task to the CD task, NC models thestudy of CD from dynamics to find communities in the dynamic network and thenconsiders whether to transfer knowledge across tasks. A test suite formultitasking NR and CD problems (MTNRCDPs) is designed to verify theperformance of NC. The experimental results conducted on the designed MTNRCDPshave demonstrated that joint NR with CD has a synergistic effect, where thenetwork structure used to inform the existence of communities is alsoinherently employed to improve the reconstruction accuracy, which, in turn, canbetter demonstrate the discovering of the community structure. The code isavailable at: https://github.com/xiaofangxd/EMTNRCD.

Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community Detection