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PurposeThe vulnerability of multinational corporation (MNC) knowledge network is one of the major causes for the failure and even the death of MNCs in the fierce global market competition. Employee turnover and knowledge loss are the triggers for the MNC knowledge network vulnerability and a matter of serious concern in the evolution and development of MNC knowledge network. The purpose of this work is to propose a valid and quantitative measurement method to investigate the influence of employee loss and knowledge loss on the vulnerability of MNC knowledge network.Design/methodology/approachMNC knowledge network is inherently a heterogeneous network where there are mainly two types of units: employees and their knowledge. Therefore, this paper establishes a weighted super-network model for MNC knowledge network to depict its heterogeneous composition. On the basis of the weighted MNC knowledge super-network, the static and dynamic vulnerability measurement methods are further proposed to investigate and evaluate MNC knowledge network vulnerability.FindingsA real case is given to illustrate the applicability of the proposed weighted MNC knowledge super-network model and the network vulnerability measurement methods. The results show the super-network model proposed in this paper can effectively embody the complex features of MNC knowledge network, and the vulnerability measurement methods can effectively investigate the influence of employee loss and knowledge loss on network vulnerability.Originality/valueFrom the perspective of super-network, researchers and practitioners can get a more systematic and deeper understanding of the MNC knowledge network and its human and knowledge resource constitute which are vital for the evolution and development of MNC. Moreover, the MNC knowledge network vulnerability measurement methods can effectively measure and analyze the influence of resource loss on network vulnerability, which can provide a helpful decision support for monitoring and managing of MNC knowledge network vulnerability to reduce its adverse effects.

In this paper, we develop a supply chain network game theory model consisting of retailers and demand markets with retailers competing noncooperatively in order to maximize their expected profits by determining their optimal product transactions as well as cybersecurity investments subject to nonlinear budget constraints that include the cybersecurity investment cost functions. The consumers at the demand markets reflect their preferences through the demand price functions, which depend on the product demands and on the average level of cybersecurity in the supply chain network. We identify the supply chain network vulnerability to cyberattacks as well as that of the individual retailers. We demonstrate that the governing Nash equilibrium conditions can be formulated as a variational inequality problem and we provide a novel alternative formulation, along with the accompanying theory. We also propose an algorithm for the alternative formulation, which yields, at each iteration, closed form expressions in product transactions, security levels, and Lagrange multipliers associated with the budget constraints. We then apply the algorithm to compute solutions to a spectrum of numerical supply chain network cybersecurity investment examples. The examples broaden our understanding of the impacts of the addition of retailers, changes in budgets, demand price functions, and financial damages, on equilibrium product transactions and cybersecurity investments, as well as on the supply chain network vulnerability and retailer vulnerability under budget constraints.

Much of the literature in recent years has examined the vulnerability of transportation networks. To identify appropriate and operational measures of nodal centrality using connectivity in the case of heavy rail systems, this paper presents a set of comprehensive measures in the form of a Degree of Nodal Connection (DNC) index. The DNC index facilitates a reevaluation of nodal criticality among distinct types of transfer stations in heavy rail networks that present a number of multiple lines between stations. Specifically, a new classification of transfer stations—mandatory transfer, non-mandatory transfer, and end transfer—and a new measure for linkages—link degree and total link degree—introduces the characteristics of heavy rail networks when we accurately expose the vulnerability of a node. The concept of partial node failure is also introduced and compare the results of complete node failure scenarios. Four local and global indicators of network vulnerability are derived from the DNC index to assess the vulnerability of major heavy rail networks in the United States. Results indicate that the proposed DNC indexes can inform decision makers or network planners as they explore and compare the resilience of multi-hubs and multi-line networks in a comprehensive but accurate manner regardless of their network sizes.

The global food trade network (FTN) is a critical infrastructure for achieving the Sustainable Development Goals (SDGs). The FTN’s vulnerability to geopolitical conflicts, public health crises, and climate change events directly impacts food security and the ability to meet the SDGs. This study aims to analyze the dynamic evolution of the vulnerability of FTN, focusing on the period from 2000 to 2022, to aim for strategies for enhancing the resilience and sustainability of the global food system. Based on complex network analysis, we examine the structural characteristics and evolution of FTN for four major crops: soybeans, wheat, rice, and maize. We identify a trend towards increased network density and regionalization, with a decline in average shortest path length (ASPL) and an increase in the average clustering coefficient (ACC). These changes indicate a shift towards a more interconnected and resilient FTN in response to various shocks, including the COVID-19 pandemic and the Russia–Ukraine conflict. The findings suggest that the global FTN has adapted to increase resilience, which is essential for achieving the SDGs related to food security and sustainable development. The study’s insights can guide policy interventions to further strengthen the network against future shocks and promote global food security.

In today’s intricate information technology landscape, the escalating complexity of computer networks is accompanied by a myriad of malicious threats seeking to compromise network components. To address these security challenges, we propose an approach that synergizes reinforcement learning and deep neural networks. Our method involves training autonomous cyber-agents to strategically attack network nodes, aiming to expose vulnerabilities and extract confidential information. We employ various off-policy deep reinforcement learning algorithms, including deep Q-network (DQN), double DQN, and dueling DQN, to train and evaluate these agents within two enterprise simulation networks provided by Microsoft. The simulations, modeled as Markov games between attack and defense, exclude human intervention. Results demonstrate that agents trained by double DQN and dueling DQN surpass baseline agents trained using traditional reinforcement learning and DQN methods. This approach not only enhances our understanding of network vulnerabilities but also lays the groundwork for future efforts to fortify computer network defense and security.

This paper highlights the relevance of connectivity and its architecture as a general conceptual framework which underlies and integrates the concepts of network vulnerability, complexity, and resilience. In particular, it will be pointed out that connectivity architecture can be considered an explicit key element for network vulnerability and shock propagation. While the relevance of the various connectivity configurations is not clearly emphasised in the dynamic complexity models of the space-economy, it appears to play a primary role in network analysis. In this regard, the emerging recognition of connectivity architecture in relation to hubs ‒ and hierarchies of hubs ‒ in a complex network will help the enhancement of network resilience. The paper develops as follows. First, the notion of network vulnerability, which refers not only to the phenomenon of shocks, but also to the propagation of shocks in a network, will be examined. Here it appears that modelling vulnerability and shock propagation, also jointly with cascading disaster models, is strongly based on connectivity issues. The question is: How can conventional (complex) system dynamic modelling, as well as network modelling, take into account these shocks and connectivity dynamics from the methodological viewpoint? A review in this respect shows how connectivity is a ‘hidden’ element in these complexity models, for example, in chaos or (dynamic) competition models, where interaction parameter values might lead to vulnerable domains and chaotic behaviour. On the contrary, connectivity and its various topologies have a distinct, primary role in network analysis. The issue of network resilience appears therefore to be the ‘response’ to vulnerability and chaos, calling for robustness and stability of the network in the presence of shocks and disruptions. Resilience analysis refers to the speed at which a network returns to its equilibrium after a shock, as well as to the perturbations/shocks that can be absorbed before the network is induced into some other equilibrium (adaptivity). Connectivity is relevant here, but not often considered in spatial economics. In order to reach a unified methodological framework, attention will finally be paid to a complementary analysis of the (dynamic) concepts of vulnerability and resilience. In this light, chaos models/properties might be seen in a positive perspective, since small changes can lead to uncertain and unstable effects, but also, thanks to connectivity, to new equilibria which are not necessarily negative. Thus, the architecture of connectivity, in its interdisciplinary insights, can be considered as a fundamental (and analytical) approach for identifying vulnerability and resilience patterns in complex networks.

In recent years, studies on network vulnerability have grown rapidly in the fields of transportation and complex networks. Even though these two fields are closely related, their overall structure is still unclear. In this study, to add clarity comprehensively and objectively, we analyze a citation network consisting of vulnerability studies in these two fields. We collect publication records from an online publication database, the Web of Science, and construct a citation network where nodes and edges represent publications and citation relations, respectively. We analyze the giant weakly connected component consisting of 705 nodes and 4,584 edges. First, we uncover main research domains by detecting communities in the network. Second, we identify major research development over time in the detected communities by applying main path analysis. Third, we quantitatively reveal asymmetric citation patterns between the two fields, which implies that mutual understanding between them is still lacking. Since these two fields deal with the vulnerability of network systems in common, more active interdisciplinary studies should have a great potential to advance both fields in the future.

With the increasing reliance on online social networks (OSNs) for communication and information sharing, the threat of cyber‐attacks—ranging from bot‐driven misinformation to account hijacking—has grown significantly. This study introduces a novel metric, the network vulnerability propagation factor (NVPF), designed to assess the risk of threat diffusion within OSNs by integrating behavioral, structural, and exposure‐based indicators. The NVPF comprises three components: node vulnerability score (NVS), connectivity index (CI), and propagation weight (PW). Their respective contributions are optimized using particle swarm optimization (PSO) to maximize detection performance. Utilizing the Cresci‐2017 Twitter dataset, which includes 1.6 million user profiles and over 37,000 labeled malicious accounts, the NVPF was calculated and integrated into a gradient boosting machine (GBM) classifier. Experimental results show that users in the top 15% of NVPF scores are 2.4 times more likely to be malicious, and the proposed model achieved an F1‐score of 0.88, precision of 0.90, and recall of 0.86, representing a 24.7% improvement over traditional centrality‐based approaches. These findings demonstrate the effectiveness and scalability of the NVPF model in enhancing cyber security risk assessment and early threat detection within dynamic OSN environments. This study introduces the NVPF metric to quantify cybersecurity risks in online social networks, demonstrating that integrating behavioral, topological, and neighbor‐based features significantly enhances threat detection accuracy—achieving a 24.7% improvement over traditional methods.

We propose a vertiport location-allocation methodology for urban air mobility (UAM) from the perspective of transportation network topology. The location allocation of vertiports within a transportation network is a crucial factor in determining the unique characteristics of UAM compared to existing transportation modes. However, as UAM is still in the pre-commercialization phase, with significant uncertainties, there are limitations in applying location-allocation models that optimize objective functions such as maximizing service coverage or minimizing travel distance. Instead, vertiport location allocation should be approached from a strategic perspective, taking into account public capital investments aimed at improving the transportation network by leveraging UAM’s distinct characteristics compared to existing urban transportation modes. Therefore, we present a methodology for evaluating the impact of vertiport location-allocation strategies on changes in transportation network topology. To analyze network topology, we use the Seoul Metropolitan railway network as the base network and construct scenarios where vertiports are allocated based on highly connected nodes and those prioritizing structurally vulnerable nodes. We then compare and analyze global network efficiency, algebraic connectivity, average shortest path length, local clustering coefficient, transitivity, degree assortativity and modularity. We confirm that while allocating vertiports based on network centrality improves connectivity compared to vulnerability-based allocation, the latter approach is superior in terms of network efficiency. Additionally, as the proportion of vertiports increases, the small-world property of the network rapidly increases, indicating that the vertiport network can fundamentally alter the structure of multimodal transportation systems. Regardless of whether centrality or vulnerability is prioritized, we observe that connectivity increase exponentially, while network efficiency changes linearly with the increase in vertiport proportion. Our findings highlight the necessity of a network-based approach to vertiport location allocation in the early stages of UAM commercialization, and we expect our results to inform future research directions on vertiport allocation in multimodal transportation networks.

Road traffic is occasionally blocked by landslide geological disasters in remote mountainous areas, causing obstruction to economic society and national defense construction. It is vital to conduct landslide geological disaster risk assessment and vulnerability research on the road network. Based on landslide geological disaster risk on the road network, this study analyzed the potential effects of the main environmental elements. Due to the lack of previous research works, this study proposed an effective, rational, and understandable multicriteria heuristic analytical hierarchy process model, fuzzy comprehensive evaluation, and frequency ratio-interactive fuzzy stack analysis for vulnerability assessment of road networks in large and complex networks. Based on the comprehensive use of geographic information technology, the road network vulnerability of Tibet in China was evaluated by introducing slope, topographic relief, normalized difference vegetation index (NDVI), annual mean precipitation, distance from river drainage, glaciers and snow, habitation, seismic center and geological fault zone, and soil erosion intensity. According to the findings of the study, the three-stage framework proposed in this study can provide correct inferences and explanations for the potential phenomena of landslide geological disasters; the geological disaster risk are unevenly distributed in the study area; the distribution of the road network vulnerability in China’s Tibet significantly differs among different cities; the high-vulnerability section presents significant regional characteristics, which overlap with the area with a high risk of landslide geological disasters, and its distribution is mostly located in traffic arteries, link aggregations, and relatively frequent human activity.