Combining rule-based reasoning and meta-path-based link prediction for heterogeneous target operational network reconstruction

Changjiang Qin (), Lianfei Yu (), Yanghe Feng (), Jincai Huang (), Guangquan Cheng () and Changjun Fan
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Changjiang Qin: College of Systems Engineering, National University of Defense Technology, Undergraduate School, National University of Defense Technology, Changsha 410073, P. R. China
Lianfei Yu: Army Academy of Border and Coastal Defence, Wulumuqi 830001, P. R. China
Yanghe Feng: College of Systems Engineering, National University of Defense Technology, Changsha 410073, P. R. China
Jincai Huang: College of Systems Engineering, National University of Defense Technology, Changsha 410073, P. R. China
Guangquan Cheng: College of Systems Engineering, National University of Defense Technology, Changsha 410073, P. R. China
Changjun Fan: College of Systems Engineering, National University of Defense Technology, Changsha 410073, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 11, 1-19

Abstract: Predicting the connections or interactions between nodes in networks remains a fundamental task in network analysis. In target systems, nodes with different types are connected with each other through different relations, as in typical heterogeneous networks. Link prediction can help identify implied information in heterogeneous target operational network (HTON) to boost network reconstruction. In view of the complex network structure of target systems and diversified types of edges alongside the low accuracy and poor interpretability issues of previous methods, we proposed an HTON reconstruction framework (HTONRP) incorporating rule-based reasoning and meta-path-based link prediction. To start with, the link rules are extracted based on operational doctrine, military theory, system formulation, etc., and then they are reasoned with initial nodes and relations (input) and the inferred target relations (output) to improve the target systems architecture. Afterwards, the data are input into the meta-path-based link prediction model of HTON, through which the architecture of target systems is further perfected. The experiments are carried out on real data instances of HTON and results suggest that the proposed method can effectively improve the prediction performance and enhance model interpretability compared to traditional baselines.

Keywords: Heterogeneous target operational network; rule-based reasoning; meta-path; link prediction; network reconstruction (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122501431

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