What Knowledge can be Transferred Between Network Reconstruction and Community Detection?

This paper focuses on inferring network structure and community structure from the dynamics of the nonlinear and complex dynamical systems, which is prominent in many fields. Many methods have been proposed to solely address these two problems, but none of them consider explicit shareable knowledge across these two tasks. Inspired by the fact that a more precise network structure may promote the accuracy of community discovery and the better communities may promote the performance of network reconstruction (NR), this paper develops an evolutionary multitasking framework to make full use of explicit shareable knowledge among these two tasks to improve their performance; we refer to this framework as EMTNRCD. In EMTNRCD, we first establish these two tasks as a multitasking NR and community detection (CD) problem where one mission is to reconstruct network structure from dynamics and the other is to discover communities from dynamics. In the process of EMTNRCD, 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, which improves the reconstruction accuracy of the NR task and the community division quality of the CD task. Moreover, to transfer knowledge from the study of the NR task to the CD task, EMTNRCD models the study of CD from dynamics as the problem of finding communities in the dynamic network and then decides whether to conduct knowledge transfer across tasks. This paper also designs a test suite for multitasking NR and CD problems (MTNRCDPs) to verify the performance of EMTNRCD. The experimental results have demonstrated that joint NR with CD has a synergistic effect.