Military heterogeneous networks often suffer from node or edge failures due to attacks, which lead to whole network topology segmentation or even paralysis. Therefore, structural invulnerability is the major technical bottleneck that impedes the combat capability of military heterogeneous system. Nodes are mainly considered in current methods, with little attention paid to edges. As such, current methods cannot accurately evaluate the structural invulnerability of military heterogeneous networks. This paper presents a new method of evaluating the survivability of military heterogeneous networks, based on network structure entropy. A model of survivability is proposed based on the network irreversibility which considers not only the nodes but also the edges. The node criticality is calculated by the level flow betweenness. Edge criticality is put forward based on the edge correlation factor and information transmission efficiency. Simulation results show that this invulnerability measure has the characteristics of high sensitivity and high precision, which will provide a theoretical basis for designing and optimizing the structure of the military heterogeneous network.

In the context of information warfare, command and control (C2) networks are exhibiting increasingly prominent multi-network dependency characteristics, leading to a growing interest in the study of interdependent C2 network models. We propose a modeling approach for interdependent C2 networks based on integrated reconnaissance-strike and OODA loop, addressing the limitations of existing edge connection strategies in effectively capturing the interdependent coupling relationships within the network. First, the interdependent relationships within the network are described, and a dual-layer structural model of interdependent C2 networks is abstracted. Second, based on the local efficiency of nodes, an edge connection strategy for sensing and firepower nodes under integrated reconnaissance-strike is proposed. Third, interdependence strength and link balance are defined, and an inter-layer coupling edge connection strategy based on interdependence strength and link balance is proposed. Finally, algorithmic simulations are designed to analyze the network properties of the model and network performance under different edge connection strategies. Simulation results demonstrate that the proposed modeling method effectively captures the interdependent characteristics of C2 networks while exhibiting enhanced network resilience against destruction.

The DR-SCIR network public opinion propagation model was employed to study the characters of S-state users stopping transmitting information for the first time and secondary transmission of immune users. The model takes into account symmetry and complexity such as direct immunization and social reinforcement effect, proposes the probability of direct immunity Psr and the probability of transform from the immune state to the hesitant state Prc, and divides public opinion information into positive public opinion and negative public opinion based on whether the public opinion information is confirmed. Simulation results show that, when direct immunity Psr = 0.5, the density of I-state nodes in the model decreased by 54.12% at the peak index; when the positive social reinforcement effect factor b = 10, the density of I-state nodes in the model increased by 16.67% at the peak index; and when the negative social reinforcement effect factor b = -10, the density of I-state nodes in the model decreased by 55.36% at the peak index. It shows that increasing the positive social reinforcement effect factor b can promote the spread of positive public opinion, reducing the negative social reinforcement effect factor b can control the spread of negative public opinion, and direct immunization can effectively suppress the spread of public opinion. This model can help us better analyze the rules of public opinion on social networks, so as to maintain a healthy and harmonious network and social environment.

Statistical correlation analysis is currently the most typically used approach for investigating the risk factors of type 2 diabetes mellitus (T2DM). However, this approach does not readily reveal the causal relationships between risk factors and rarely describes the causal relationships visually. Considering the superiority of reinforcement learning in prediction, a causal discovery approach with reinforcement learning for T2DM risk factors is proposed herein. First, a reinforcement learning model is constructed for T2DM risk factors. Second, the process involved in the causal discovery method for T2DM risk factors is detailed. Finally, several experiments are designed based on diabetes datasets and used to verify the proposed approach. The experimental results show that the proposed approach improves the accuracy of causality mining between T2DM risk factors and provides new evidence to researchers engaged in T2DM prevention and treatment research.

Cascading failure models for command and control networks (C2 networks) continue to be a challenging and important research area. Current solutions share a common limitation because the solutions focus only on the importance of each node in isolation using one index rather than considering the contribution degree of neighboring nodes, which makes the initial load definition inaccurate and affects the cascading invulnerability of the network. To address this limitation, a new cascading failure model for C2 networks is proposed. The new model CFM_AdjM, which is based on an m-order adjacency matrix, proposes a method of initial load definition using the contribution degree of m-order neighboring nodes and defines the nonlinear load capacity model according to the nonlinear relationship between load and capacity. Finally, the influence of model parameters on the cascading failure of C2 networks is analyzed through simulation, and the results demonstrate that the new model effectively resists the cascading failure and enhances the survivability of the network by defining the initial load and adjusting the coefficient appropriately.