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.

翻译：本文聚焦于从复杂系统动力学中联合推断网络与社区结构。尽管已有多种方法分别解决这两个问题，但尚未有方法考虑这两项任务间显式可共享的知识。动力学驱动的社区发现（CD）与网络重构（NR）是天然协同的任务，为此我们提出了进化多任务NR与CD框架，即网络协作者（NC）。在NC过程中，NR任务显式地向CD任务迁移若干更优的网络结构，而CD任务则显式地向NR任务迁移更优的社区结构以提供辅助。此外，为将知识从NR任务迁移至CD任务，NC对动力学驱动下的CD研究进行建模，以识别动态网络中的社区，并据此判断是否进行跨任务知识迁移。我们设计了面向多任务NR与CD问题的测试套件（MTNRCDPs）以验证NC的性能。在设计的MTNRCDPs上开展的实验结果表明，将NR与CD联合处理具有协同效应：用于指示社区存在的网络结构可天然提升重构精度，而更优的重构结果又能进一步揭示社区结构的发现。代码已开源：https://github.com/xiaofangxd/EMTNRCD。