The combat system-of-systems in high-tech informative warfare consists of different types of combat systems connected via multiple interconnections, which can be abstracted as types of complex heterogeneous networks. Research on the functional robustness of heterogeneous combat networks (HCNs) is of significant military value for optimizing operation process planning and for improving network survivability in complex electromagnetic battle fields. This study presents an integrated framework, known as the HCN functional robustness (HCNFR) framework to measure the functional robustness of HCNs under various attacking strategies. Specifically, a heterogeneous network model is introduced to characterize HCNs considering different types of functional entities and information flows. Based on this setup, the rescaled operational capability index (ROCI) is first proposed to evaluate the functional robustness of HCNs, and the criterion for functional disintegration of HCNs is established by summing the values of ROCI during the attacking process. Finally, the reliability and effectiveness of HCNFR are illustrated with an empirical case study. The results provide useful insights for operational guidance and design of a more functional and robust combat system-of-systems.

Modern military operations in high-tech information warfare settings are dynamic processes involving various types of combat systems connected via multiple channels, which can be abstracted as a type of complex temporal combat network (TCN). The equipment contribution analysis in TCNs is of significant military value for optimizing operation process planning and improving network resilience in complex electromagnetic battlefields. This paper presents an integrated framework called capability oriented equipment contribution analysis (CECA) to analyze key equipment in TCNs. Specifically, a temporal network model is proposed to characterize TCNs by considering their dynamic nature and the heterogeneity capabilities of different types of functional entities during military operations. Based on this model, an operation capability contribution index is proposed to measure the contribution of each piece of equipment involved in executing operational tasks. Finally, the reliability and effectiveness of the CECA are demonstrated based on a TCN case study. This paper provides a detailed and precise quantitative analysis of the equipment contributions in TCNs, which yields useful insights for operation guidance and designing a more resilient combat system-of-systems.

The combat system-of-systems (CSoSs) in high-tech information warfare, composed of different types of combat systems connected through multiple interconnections, can be abstracted as a type of complex heterogeneous network. Research on the disintegration of the operational capability of a heterogeneous combat network (HCN) is of significant military value for optimizing the planning of operation process and improving network survivability in a complex electromagnetic battlefield. Accordingly, this paper presents an integrated framework called HCN operational capability disintegration based on link prediction (OCDLP) for modeling and solving the disintegration problem regarding the operational capability of an HCN. More specifically, a heterogeneous network model of a combat network under incomplete information is first established, considering different types of functional entities and information flows. Based on this model, a link prediction model is then applied to recover the original combat network structure while taking advantage of the observed information. Next, a disintegration evaluation metric of the operational capability of an HCN is elicited to evaluate the attack efficiency after a link prediction. Last, the reliability and effectiveness of the OCDLP are illustrated through a case study. The results provide useful insight into the operation guidance and design of a more resilient CSoSs.

The increasingly available large-scale bibliographic data that generate a heterogeneous network provide opportunities to detect, track and predict the evolution of science. Recently, many efforts have been devoted to quantify the impact of scientific papers within different citation time windows. However, the complex patterns of the citation network make it difficult to predict future citations on the basis of a short time window. Accordingly, we present a data-centric methodology to predict long-term scientific impact by combining numerous bibliographic features and convolutional neural network. More specifically, we first expand the input features from the annual citation records to the features of the whole heterogeneous bibliographic information network which completely represents the topology structure of academic activities. Then, a convolutional neural network model is designed to capture the complex nonlinear relationships between early network features and final cumulative citation count. Last, we conduct an experiment on papers of Markov Chain from 1980 to 1985. The result shows the prediction performance can be improved by 5% to baseline models under the same problem definition and with the same dataset. Meanwhile, the long-term scientific impacts are strongly correlated with its recognition by authoritative authors or venues in the early stage.