The structural robustness of the combat network of weapon system-of-systems (CNWSOS) has been widely used to characterise its operation ability and survivability in the integrated joint operations. In this paper, after introducing the concept of the operation loop, a directed CNWSOS network model is first put forward. Then, the directed natural connectivity, a new measure of the CNWSOS robustness that extends the concept of natural connectivity to the directed combat networks, is proposed to provide both a definite physical meaning and a simple mathematical formulation. Last, the feasibility and effectiveness of the proposed structural robustness measure are demonstrated using both numerical experiments and an empirical case study. The directed natural connectivity facilitates a more sensitive and precise quantitative analysis of the structural robustness, which in turn provides useful insights for designing a more robust CNWSOS, including improving the accuracy of intelligence acquisition, enhancing the communication capabilities of weapon systems, and strengthening the capability of precision strikes.

A novel executable modeling approach is proposed for system-of-systems architecture by taking full advantage of the Department of Defense Architecture Framework (DoDAF) Meta-model (DM2) and defining the common model transformation specification at the metamodel level. This methodology provides more flexibility and adaptability for the automated construction of executable models directly from the architectural data rather than from static models. More specifically, the architectural data metamodel is first established to guide architectural data modeling of core data elements and associations in DM2 as the common and consistent data dictionary for architecture modeling, while the executable formalism metamodel is designed to formally define executable models. Then, the mapping rules between both metamodels are presented as the common transformation specification regardless of which modeling language or methodology is employed in developing architectural descriptions. Finally, eXtensible Markup Language (XML) technology, like XML schema languages and eXtensible Stylesheet Language specifications, is discussed to facilitate the automated transformation of executable models from architectural instance data. The feasibility of the proposed approach is illustrated using a published example where colored Petri net (CPN) is used as the executable formalism.

A novel approach based on the graph model for conflict resolution (GMCR) methodology is proposed to address the problem of multistakeholder system portfolio decision analysis encountered in architecting a system of systems (SoS) with desired capabilities. More specifically, a flexible four-process framework for capability-based SoS architecting containing interactive portfolio decision analysis to promote multistakeholder design negotiations on system portfolio selections is presented. By taking full advantage of the inherent realistic and flexible design of the GMCR paradigm, an interactive portfolio decision analysis approach is designed to facilitate the systematic modeling and analysis of system portfolio decisions at the SoS level in order to achieve potential compromises among all key stakeholders having disparate preferences and interacting according to different conflict behavior patterns. This approach permits the prediction of possible mutually agreeable system portfolios for SoS architecture development. Last, the feasibility of the proposed approach is demonstrated using an illustrative example.

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.