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The maritime kill chain constitutes the core of the current joint maritime combat system, representing the practical application of the OODA loop in the context of informationized warfare. It embodies the operational mechanism of the joint combat system, where information flow dominates energy flow in a closed-loop operation. Determining the critical nodes of the maritime kill chain is the prerequisite for achieving comprehensive maritime dominance. Currently, the assessment of key nodes within maritime kill chains employs theoretical analysis and complex network methods. Complex network analysis techniques, which often utilize a node-by-node deletion approach to evaluate node importance, suffer from issues such as low search efficiency and limited applicability to small-scale systems. This paper takes the NIFC-CA system as an example to construct a typical maritime kill chain model. The importance of kill chain nodes is ranked using a complex network assessment method of integration centrality, and key nodes are identified. Additionally, graph theory and network analysis tools from Python’s Network library are utilized during the assessment to significantly enhance the computational efficiency.

The construction supply chain confronts interruption risks that raise significant concerns regarding industry safety and stability. Consequently, exploring risk management strategies from both enterprise and supply chain network perspectives is crucial. This study employs complex network theory and the cascade failure model to propose a methodology tailored to the unique characteristics of the construction supply chain, facilitating the identification of key risk nodes and the conduct of invulnerability analyses. By evaluating the importance of construction enterprise nodes and their risk propagation ability during cascade failures, this method enables the comprehensive identification of key risk node enterprises within the construction supply chain network. Furthermore, this study examines and discusses strategies for enhancing network invulnerability by taking into account node capacity, load, and resilience. Empirical results indicate that the key nodes and risk nodes in the construction supply chain network are mainly located upstream and downstream, displaying specific distribution patterns. In addition to core enterprises, key risk nodes comprise some strong suppliers at the intermediary and lower tiers of the supply chain. Adjustments to node enterprise parameters like capacity, load, and resilience have diverse impacts on the invulnerability of the construction supply chain network. This study clarifies the distribution patterns of key risk nodes within the construction supply chain network and the variations in network invulnerability under particular conditions, providing valuable insights for risk management decision-making. [ABSTRACT FROM AUTHOR]

Mining key nodes in complex networks has been a hotly debated topic as it played an important role in solving real-world problems.However, the existing key node mining algorithms focused on finding key nodes from a global perspective.This approach became problematic for large-scale social networks due to the unacceptable storage and computing resource overhead and the inability to utilize known query node information.A key node mining algorithm based on multiple query nodes was proposed to address the issue of key suspect mining.In this method, the known suspects were treated as query nodes, and the local topology was extracted.By calculating the critical degree of non-query nodes in the local topology, nodes with higher critical degrees were selected for recommendation.Aiming to overcome the high computational complexity of key node mining and the difficulty of effectively utilizing known query node information in existing methods, a two-stage key node mining algorithm based on multi-query was proposed to integrate the local topology information and the global node aggregation feature information of multiple query nodes.It reduced the calculation range from global to local and quantified the criticality of related nodes.Specifically, the local topology of multiple query nodes was obtained using the random walk algorithm with restart strategy.An unsupervised graph neural network model was constructed based on the graphsage model to obtain the embedding vector of nodes.The model combined the unique characteristics of nodes with the aggregation characteristics of neighbors to generate the embedding vector, providing input for similarity calculations in the algorithm framework.Finally, the criticality of nodes in the local topology was measured based on their similarity to the features of the query nodes.Experimental results demonstrated that the proposed algorithm outperformed traditional key node mining algorithms in terms of time efficiency and result effectiveness.

Identifying the most important nodes in command and control networks, or evaluating the relative importance of one node compared to others, is a key issue in the study of these networks. Various effective models have been proposed to address this. Traditional gravity models, which consider node degree as the sole factor representing mass, are limited in their ability to accurately identify core nodes, often misclassifying nodes with local high clustering characteristics as central nodes. To improve accuracy, this paper proposes the Coulomb Force Model (CFM), which is inspired by the gravity model but incorporates Coulomb’s law.The CFM uses a dynamic radius method to determine the influence range of each node more precisely. The charge quantity, representing node importance, is determined based on node attributes, and the polarity of the charge indicates the influence on neighboring nodes. This approach introduces intrinsic node information to more accurately measure the differences in node influence, thereby enhancing the algorithm’s precision. We validated the effectiveness and accuracy of the proposed algorithm through comparative experiments on multiple datasets, using classic influence propagation models and evaluation metrics. The results demonstrate the improved performance of the CFM in identifying influential nodes in command and control networks.

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The construction supply chain confronts interruption risks that raise significant concerns regarding industry safety and stability. Consequently, exploring risk management strategies from both enterprise and supply chain network perspectives is crucial. This study employs complex network theory and the cascade failure model to propose a methodology tailored to the unique characteristics of the construction supply chain, facilitating the identification of key risk nodes and the conduct of invulnerability analyses. By evaluating the importance of construction enterprise nodes and their risk propagation ability during cascade failures, this method enables the comprehensive identification of key risk node enterprises within the construction supply chain network. Furthermore, this study examines and discusses strategies for enhancing network invulnerability by taking into account node capacity, load, and resilience. Empirical results indicate that the key nodes and risk nodes in the construction supply chain network are mainly located upstream and downstream, displaying specific distribution patterns. In addition to core enterprises, key risk nodes comprise some strong suppliers at the intermediary and lower tiers of the supply chain. Adjustments to node enterprise parameters like capacity, load, and resilience have diverse impacts on the invulnerability of the construction supply chain network. This study clarifies the distribution patterns of key risk nodes within the construction supply chain network and the variations in network invulnerability under particular conditions, providing valuable insights for risk management decision-making.

In social network analysis, identifying the important nodes (key nodes) is a significant task in various applications. There are three most popular related tasks named influential node ranking, influence maximization, and network dismantling. Although these studies are different due to their own motivation, they share many similarities, which could confuse the non-domain readers and users. Moreover, few studies have explored the correlations between key nodes obtained from different tasks, hindering our further understanding of social networks. In this paper, we contribute to the field by conducting an in-depth survey of different kinds of key nodes through comparing these key nodes under our proposed framework and revealing their deep relationships. First, we clarify and formalize three existing popular studies under a uniform standard. Then we collect a group of crucial metrics and propose a fair comparison framework to analyze the features of key nodes identified by different research fields. From a large number of experiments and deep analysis on twenty real-world datasets, we not only explore correlations between key nodes derived from the three popular tasks, but also summarize insightful conclusions that explain how key nodes differ from each other and reveal their unique features for the corresponding tasks. Furthermore, we show that Shapley centrality could identify key nodes with more generality, and these nodes could also be applied to the three popular tasks simultaneously to a certain extent. [ABSTRACT FROM AUTHOR]

Cordycepin is one of the core high-value-added secondary metabolites produced by fungi belonging to the genus Cordyceps. Compared with other industrial strains, Cordyceps militaris benefits from the innate metabolic flux in the synthesis of adenosine analogs such as cordycepin. In recent years, with the development of omic analysis technology and Cordyceps militaris gene-editing technology, the metabolic network of cordycepin synthesis, particularly the key substrate synthesis pathway, has been completely analyzed. Therefore, this review, the known metabolic network of cordycepin is modularized, dividing it into the central carbon metabolic pathway, inosinate (IMP) pathway, and substrate biosynthesis pathway, completely describing its metabolic network. The composition of cordycepin precursor and the influence of multiple dispersion pathways and key nodes on the synthesis of cordycepin have been analyzed, and the synthesis and flow direction of IMP substances have been systematically elaborated. This shows that the synthesis and metabolism of IMP is the essential node in cordycepin synthesis. These discoveries provide a comprehensive foundation for optimizing the metabolism network of Cordyceps militaris through metabolic engineering and synthetic biology strategies, for developing a stable and high-yielding Cordyceps militaris strain in the future. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture, and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to expand. There are key node users in social networks. Compared with ordinary users, their influence is greater, their radiation range is wider, and their information transmission capabilities are better. The key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking sites. In order to solve the problems of incomplete evaluation factors, poor recognition rate and low accuracy of key nodes of social networking sites, this paper establishes a social networking site key node recognition algorithm (SNSKNIS) based on PageRank (PR) algorithm, and evaluates the importance of social networking site nodes in combination with the influence of nodes and the structure of nodes in social networks. This article takes the Sina Weibo platform as an example, uses the key node identification algorithm system of social networking sites to discover the key nodes in the social network, analyzes its importance in the social network, and displays it visually. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem of high time-consuming and resource-consuming quantitative calculation of large-scale industrial control network attack graphs, a key path analysis method for large-scale industrial control networks was proposed.Firstly, the idea of cut set was used to calculate the key nodes set of Bayesian attack graph by combining the atomic attack income in industrial control network, which solved the problem that the current cut set algorithm only considers the key nodes in graph structure.Secondly, a dynamic updating strategy of Bayesian attack graph which only updated the attack probability of key nodes was proposed to efficiently calculate the attack probability of the whole graph and analyze the key path of attack graph.The experimental results show that the proposed method can not only ensure the reliability of the calculation results of large-scale industrial control attack graphs, but also can significantly reduce the time consumption and have a significant improvement in the calculation efficiency.

To enhance the accuracy of performance analysis of regional airline network, this study applies complex network theory and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to investigate the topology of regional airline network, constructs node importance index system, and clusters 161 airport nodes of regional airline network. Besides, entropy power method and approximating ideal solution method (TOPSIS) is applied to comprehensively evaluate the importance of airport nodes and complete the classification of nodes and identification of key points; adopt network efficiency, maximum connectivity subgraph and network connectivity as vulnerability measurement indexes, and observe the changes of vulnerability indexes of key nodes under deliberate attacks and 137 nodes under random attacks. The results demonstrate that the decreasing trend of the maximum connectivity subgraph indicator is slower and the decreasing trend of the network efficiency and connectivity indicators is faster when the critical nodes of the regional airline network are deliberately attacked. Besides, the decreasing trend of the network efficiency indicator is faster and the decreasing trend of the maximum connectivity subgraph indicator is slower when the nodes of four different categories are randomly attacked. Finally, it is proposed to identify and focus on protecting critical nodes in order to better improve the security level of regional airline system. [ABSTRACT FROM AUTHOR]

Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem of high time-consuming and resource-consuming quantitative calculation of large-scale industrial control network attack graphs, a key path analysis method for large-scale industrial control networks was proposed. Firstly, the idea of cut set was used to calculate the key nodes set of Bayesian attack graph by combining the atomic attack income in industrial control network, which solved the problem that the current cut set algorithm only considers the key nodes in graph structure. Secondly, a dynamic updating strategy of Bayesian attack graph which only updated the attack probability of key nodes was proposed to efficiently calculate the attack probability of the whole graph and analyze the key path of attack graph. The experimental results show that the proposed method can not only ensure the reliability of the calculation results of large-scale industrial control attack graphs, but also can significantly reduce the time consumption and have a significant improvement in the calculation efficiency.

Graph convolutional networks have achieved remarkable performance in action recognition from skeleton videos. However, most of the existing GCN-based methods improve performance by increasing model parameters, which require a high amount of data. This means that they usually perform poorly on small sample learning tasks. In this paper, we propose a novel enhanced data guidance algorithm to improve the performance of the GCN-based method on small sample datasets. These enhanced data perform coordinate transformation on the skeleton to obtain robustness to scale, rotation and translation. The proposed guidance algorithm allows the target model to learn the advantages of enhanced data and reduce the complexity of the task. We also propose a new key node method, which can select key joints and frames in the spatial and temporal dimensions respectively. This removes the redundant information of the skeleton sequence and significantly reduces the computational cost. Furthermore, the combination of key nodes and enhanced data can greatly reduce the demand for training data. The recognition accuracy rates of 94.81% and 94.19% have been achieved on the public MSR Action3D and UTD-MHAD datasets, respectively. This result proves that our method is significantly better than mainstream 3D action recognition methods. [ABSTRACT FROM AUTHOR]

This paper studies a mechanism design problem with networked agents and stochastically evolving private information. In contrast to the canonical mechanism design theory focusing only on information asymmetry, we also pay attention to the topology of social network among agents. We find that the standard first-order approach for mechanism design is invalid in dynamic environment. As a remedy, a novel ironing technique is proposed, which produces a perfectly sorting allocation. Based on the optimal dynamic mechanism obtained, we define and compare some important nodes and edges in a network for different ranges of synergy parameter. We further discuss the network intervention problem, in which the principal can intervene to change the ex-ante distribution of individual types. [ABSTRACT FROM AUTHOR]

Taking a large three story underground space build in an old port zone in Shanghai as an example, the micro simulation software of VISSIM was used to study the traffic organization design of underground space, and the key nodes and capacity index parameters for evaluation were determined, and then exponential function was adopted to evaluate the capacity of each node. Finally, the level of service of the key nodes in the parking lot was evaluated by the vehicle delay in the simulation environment, and the parking guidance of underground space was optimized by combining with the mathematical programming method. The technology of the scheme is reasonable and well applied, which can provide technical reference for similar projects [ABSTRACT FROM AUTHOR]

The topology of multi-region opportunistic sensor networks is evolving, and it is difficult to identify the key nodes in the networks by traditional key node identification methods. In this paper, a novel method based on the improved TOPSIS method is proposed to identify the key node from the ferry node. The dynamic topology information is represented by the graph model which is modeled by the temporal reachable graph. Based on the temporal reachable graph, three attributes are constructed to identify the key node, which are average degree, betweenness centrality and message forwarding rate. The game theory with a combination weighting method is employed to combine the subjective weight and objective weight, so as to obtain the combined weight of each attribute. The TOPSIS method is improved by the combined weight. The key node is identified by the improved TOPSIS. The experiments in three simulation situations show that, compared with the TOPSIS method and MADM_TOPSIS method, the proposed method has better accuracy for the key node identification in the network. [ABSTRACT FROM AUTHOR]

When there is a segmentation phenomenon in the network,the increase in throughput easily leads to network congestion.The key problem that causes network segmentation is that there are key nodes in the network.The key nodes are public nodes of a large number of network routes,which lead to the increase of energy consumption of key nodes and the decrease of network communication efficiency.A node mobility scheme based on dependency graphs to compensate for network segmentation was proposed.Firstly,the key nodes that cause congestion in the network were determined through the dependency graph.Then,the neighboring points of the key nodes were selected.Under the condition that the topology of the nodes were unchanged,moving in the divection of the shortest connected distance to establish a backup route of the key node position.The simulation results show that compared with the existing node mobility schemes,this scheme has significant performance improvements in network connectivity,coverage and network life cycle.

Abstract Recently, network vulnerability or security has attracted much attention in various networked systems, and especially in security related attacks or protections, there are a set of influential nodes that can remarkably break the network connectivity. In this work, we firstly present eight attack mechanisms including target attack, random failure, betweenness based attack, closeness based attack, PageRank based attack, k-shell based attack, greedy algorithm, and low-degree attack. Secondly, inspired by the dynamic node removal process, we propose to recalculate the metrics for every node removal strategy, and evaluate the network robustness against all these heuristic attack strategies with and without recalculations in scale-free networks, random networks, and many real network models. The simulations indicate that most of the attack strategies with recalculations appear to imperil the network structure security more. Furthermore, considering that key node set mining is very critical for network structure protections, we employ minimum number of key nodes (MNKN) metric to further discuss the network vulnerability against all the attack strategies with or without recalculations. The results show that the critical nodes' group can be more efficiently found under the PageRank based attack with recalculations than under other attack disciplines with or without recalculations in most of the classic and real network models. This work investigates network structure vulnerability and security from a new perspective, and has potential applications into network structure protection or planning. Highlights • Network robustness is evaluated under various attack strategies. • Critical nodes' group mining problem is proposed and extensively discussed. • Potential applications are possible in network security area. [ABSTRACT FROM AUTHOR]

There are some key nodes in P2P streaming networks， which play an important role in network security and network communication. So identifying key nodes in the network is very crucial. The traditional method has huge time overhead for key node identification in large-scale networks and cannot guarantee real-time performance. We propose a key node identification algorithm in P2P stream¬ing media networks. Combining with the network structure characteristics of the hybrid mode，we use a region-based computing model to solve the huge time-consumption problem caused by the excessive net-work scale. The importance of nodes is quantitatively described according to the contribution and propa¬gation capacity of nodes. Simulation results show that the proposed algorithm can quickly obtain the re¬sults of node importance ranking and effectively identify the key nodes in P2P streaming networks. [ABSTRACT FROM AUTHOR]

A distributed key nodes discovery algorithm was proposed(DABC) which was implemented on Graphlab.Due to the good scalability,the scale of graph supported by algorithm was enlarged significantly.The parallel processing also enhances the speed of calculation.Experiment results show that proposed algorithm can achieve up to 4 times per-formance improvement compared with the traditional centralized key node discovery algorithm.

Finding the key node set that is connected with a given number of external control sources for driving complex networks from initial state to any predefined state with minimum cost, known as minimum-cost control problem, is critically important but remains largely open. By defining an importance index for each node, we propose revisited projected gradient method extension (R-PGME) in Monte-Carlo scenario to determine key node set. It is found that the importance index of a node is strongly correlated to occurrence rate of that node to be selected as a key node in Monte-Carlo realizations for three elementary topologies, Erdős–Rényi and scale-free networks. We also discover the distribution patterns of key nodes when the control cost reaches its minimum. Specifically, the importance indices of all nodes in an elementary stem show a quasi-periodic distribution with high peak values in the beginning and end of a quasi-period while they approach to a uniform distribution in an elementary cycle. We further point out that an elementary dilation can be regarded as two elementary stems whose lengths are the closest, and the importance indices in each stem present similar distribution as in an elementary stem. Our results provide a better understanding and deep insight of locating the key nodes in different topologies with minimum control cost. [ABSTRACT FROM AUTHOR]

As the basic inter-domain routing protocol, security issues of BGP (border gateway protocol) have drawn much attention from academic community, especially the trade-off between security and difficulty in implementation. Having pinned down the inadequacy of the traditional solution S-BGP, on the basis of the definition of KN, this paper utilized KN's special function of routing security validation and put forward a lightweight scheme-secured inter-domain routing mechanism SR-KN. As a result, under the premise of the security of BGP, the results of analysis and comparison show that for the whole network, the scale of certification and the storage are reduced and the convergence performance is improved. [ABSTRACT FROM AUTHOR]

Recent global warming has made it possible to exploit and utilize resources in the Arctic Northwest Passage. However, the harsh natural environment in this sea area poses a major threat to safety during navigation. Although this passage is extensive, the natural environmental state of few key nodes affect the navigability of the entire passage. In this paper, we describe dynamic assessment of natural environmental risks of key nodes in the Arctic Northwest Passage using Dynamic Bayesian Network (DBN). Specifically, index selection and data processing, determination of key navigation nodes, calculation of evidence-based reasoning and verification of DBN-model are discussed. Results show that the DBN-model effectively handles uncertainty of information, and generates highly accurate inference results. In addition, it integrates historical information in the reasoning process, enables accumulation of information, reduces the influence of data errors on the final result, and makes the result closer to the real value. Overall, this model provides an important reference for judging the comprehensive risks of natural environment at key nodes. • The natural environment risk index system of key nodes in the Northwest Passage was constructed. • According to historical navigation data, 5 key nodes of Ba-Lan key area were selected. • The DBN-model has good natural environment risk status assessment and predictive performance. • The DBN-model can improve the accuracy of reasoning and reduce the uncertainty of reasoning. • The DBN-model has powerful update capabilities based on information detection. [ABSTRACT FROM AUTHOR]