论文标题
网络合作者：网络重建与社区检测之间的知识转移
Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community Detection
论文作者
论文摘要
本文着重于复杂系统动态的共同推断网络和社区结构。尽管已经设计了许多方法来仅解决这两个问题，但它们都没有考虑在这两个任务中明确可共享的知识。动态和网络重建（NR）的社区检测（CD）是自然的协同任务，它激发了所提出的进化多任务NR和CD框架，称为网络协作者（NC）。在NC的过程中，NR任务明确转移了CD任务的几个更好的网络结构，并且CD任务明确传输了更好的社区结构以帮助NR任务。此外，要将知识从NR任务转移到CD任务，NC将CD的研究从动力学中建模，以在动态网络中找到社区，然后考虑是否跨任务转移知识。多任务NR和CD问题（MTNRCDPS）的测试套件旨在验证NC的性能。对设计的MTNRCDPS进行的实验结果表明，与CD的关节NR具有协同作用，在此效应中，用于告知社区存在的网络结构也固有地用于提高重建准确性，这反过来又可以更好地证明对社区结构的发现。该代码可在以下网址提供：https：//github.com/xiaofangxd/emtnrcd。
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.
