Identification Of Key Nodes Research Articles

Evaluation of nosocomial infections through contact patterns in a small animal hospital using social network analysis and genotyping techniques

Nosocomial infections or hospital-acquired infections (HAIs) are common health problems affecting patients in human and animal hospitals. Herein, we hypothesised that HAIs could be spread through human and animal movement, contact with veterinary medical supplies, equipment, or instruments. We used a combination of social network analysis and genotyping techniques to find key players (or key nodes) and spread patterns using Escherichia coli as a marker. This study was implemented in the critical care unit, outpatient department, operation room, and ward of a small animal hospital. We conducted an observational study used for key player determination (or key node identification), then observed the selected key nodes twice with a one-month interval. Next, surface swabs of key nodes and their connecting nodes were analysed using bacterial identification, matrix-assisted laser desorption/ionisation-time of flight mass spectrometry, and pulsed-field gel electrophoresis. Altogether, our results showed that veterinarians were key players in this contact network in all departments. We found two predominant similarity clusters; dendrogram results suggested E. coli isolates from different time points and places to be closely related, providing evidence of HAI circulation within and across hospital departments. This study could aid in limiting the spread of HAIs in veterinary and human hospitals.

Identification of key nodes in abnormal fund trading network based on improved pagerank algorithm

The traditional network theory is unsuitable for money laundering path analysis because of the complexity of financial transaction. The abnormal accounts are identified only by effectively mining the characteristics of fund flow. The importance of transaction node in the whole illegal capital flow is equivalent to that of the connection among these nodes. Through combining the page rank algorithm with the characteristics of fund flow, an improved weighted and iterative initial value mechanism is designed to calculate the transaction heat value of the account, thus the abnormal capital transaction account is screened out. When the method is applied to a money laundering case, the results show the improved page rank algorithm can effectively screen out the key account nodes in the money laundering network and provide clues for the monitoring of money laundering crimes.

Identifying key node in multi-region opportunistic sensor network based on improved TOPSIS

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.

Identifying Central Nodes in Directed and Weighted Networks

An issue of critical interest in complex network analysis is the identification of key players or important nodes. Centrality measures quantify the notion of importance and hence provide a mechanism to rank nodes within a network. Several centrality measures have been proposed for un-weighted, un-directed networks but applying or modifying them for networks in which edges are weighted and directed is challenging. Existing centrality measures for weighted, directed networks are by and large domain-specific. Depending upon the application, these measures prefer either the incoming or the outgoing links of a node to measure its importance. In this paper, we introduce a new centrality measure, Affinity Centrality, that leverages both weighted in-degrees as well as out-degrees of a node’s local neighborhood. A tuning parameter permits the user to give preference to a node’s neighbors in either incoming or outgoing direction. To evaluate the effectiveness of the proposed measure, we use three types of real-world networks - migration, trade, and animal social networks. Experimental results on these weighted, directed networks demonstrate that our centrality measure can rank nodes in consonance to the ground truth much better than the other established measures

A Key Business Node Identification Model for Internet of Things Security

Based on the research of business continuity and information security of the Internet of Things (IoT), a key business node identification model for the Internet of Things security is proposed. First, the business nodes are obtained based on the business process, and the importance decision matrix of business nodes is constructed by quantifying the evaluation attributes of nodes. Second, the attribute weights are improved by the analytic hierarchy process (AHP) and entropy weighting method from subjective and objective dimensions to form the combination weight decision matrix, and the analytic hierarchy process and entropy weighting VIKOR (AE-VIKOR) method are used to calculate the business node importance coefficient to identify the key nodes. Finally, according to the NSL-KDD dataset, the network security events of IoT network intrusion detection based on machine learning are monitored purposefully, and after the information security event occurs in the smart mobile phone, which impacts through IoT on the business system, the impact of the key business node on business continuity is analyzed, and the business continuity risk value is calculated to evaluate the business risk to prove the effectiveness of the model. The experimental results of the civil aviation departure business show that the AE-VIKOR method can effectively identify key business node, and the impact of the key business node on business continuity is analyzed, which further proves the efficiency and accuracy of the model in identifying the key business node.

Research on the Identification of Key Nodes in the Process of WeChat Epidemic Information Dissemination: A Supernetwork Perspective

It is of great significance to comprehensively and reasonably identify the key nodes in the WeChat epidemic information dissemination system. First, the recognition results can be used to guide the spread of epidemic-related information in WeChat, such as accelerating the spread of valuable information or monitoring the spread of rumors. Secondly, the analysis of key nodes helps us understand the evolution of the epidemic information dissemination network in WeChat, and the analysis of key nodes also helps us understand the modes and methods of epidemic-related information dissemination in WeChat. Finally, the results of these studies may be generalized to other fields of social life. This paper analyzes the composition of and relationship between epidemic-related information dissemination systems in WeChat and proposes a Supernetwork model for WeChat epidemic information dissemination on this basis. In this study, a comprehensive identification method of key nodes of the WeChat epidemic information dissemination system under the Supernetwork vision was constructed, and the method is analyzed and verified through examples in this paper.

Key node identification of wireless sensor networks based on cascade failure

Wireless sensor networks (WSNs) have become one of the core technologies of the internet of things (IoT) system. They are information generation and acquisition systems used by the IoT to sense and identify the surrounding environment. They are also sensor technology, embedding computing technology, communication technology and important product in the development of Internet technology, which have made the whole society more intelligent and humanized. WSNs are multi-hop self-organizing networks consisting of a large number of micro-sensor nodes deployed in the monitoring area. They can collaboratively sense, collect and process the monitored objects and transmit them to the observers. In this paper, we use the cascade failure method to find the key nodes in the WSNs. First, a complex network cascade failure model based on load redistribution is proposed. Differences from the existing model are as follows: (1) for each node, an overload function is defined; (2) the evolution of the network topology is replaced by node weight evolution. Based on the cascade failure model, a method for evaluating the importance of complex load network nodes is proposed and a new definition of node importance is given. This method helps to discover some potential “critical nodes” in the network. The final experimental analysis verifies the effectiveness and feasibility of the proposed method.

Biomarkers for Liquid Biopsies of Pituitary Neuroendocrine Tumors.

Pituitary neuroendocrine tumors (PitNET) do not only belong to the most common intracranial neoplasms but seem to be generally more common than has been thought. Minimally invasive liquid biopsies have the potential to improve their early screening efficiency as well as monitor prognosis by facilitating the diagnostic procedures. This review aims to assess the potential of using liquid biopsies of different kinds of biomarker species that have only been obtained from solid pituitary tissues so far. Numerous molecules have been associated with the development of a PitNET, suggesting that it often develops from the cumulative effects of many smaller genetic or epigenetic changes. These minor changes eventually pile up to switch critical molecules into tumor-promoting states, which may be the key regulatory nodes representing the most potent marker substances for a diagnostic test. Drugs targeting these nodes may be superior for the therapeutic outcome and therefore the identification of such pituitary-specific cellular key nodes will help to accelerate their application in medicine. The ongoing genetic degeneration in pituitary adenomas suggests that repeated tumor profiling via liquid biopsies will be necessary for personalized and effective treatment solutions.

Multi-objective optimization model in a heterogeneous weighted network through key nodes identification in overlapping communities

Nowadays, it is possible to easily utilize positive and negative effects of neighbors on a social network to maximize diffusion of a novel product and profit of the seller. Hence, this paper aims to introduce a new mathematical model for a product pricing in non-competitive environment having multiple goals. The proposed model is designed while there are a monopole seller and several heterogeneous customers for a novel product. Considering various criteria, these customers are able to purchase the novel product including price, product quality, urgent need to have the product, and positive/negative externalities received from the neighbors. Moreover, they are able to comment in case of satisfaction or dissatisfaction with the product. However, the extent of influence depends on strength of the relations with neighbors that is considered in the proposed model with complete information and quantitative values. Proportionate to activating the neighbors, referral bonus is considered from the seller. To find influential nodes for the influence and exploit strategy implementation we propose a new overlapping community detection algorithm. In this algorithm, a new overlapping score based on non-member neighbor nodes connectivity is introduced to identify overlapping communities. Finally, we evaluate the efficiency of the proposed model, by implementing the proposed community detection algorithm in a real-world dataset. The results show that it is possible to obtain desired selling price in a fashion that maximum diffusion in the network happens and the seller achieves his desired profit under various management viewpoints.

Identification of Key Flight Conflict Nodes Based on Complex Network Theory

The air traffic density in the terminal area is high and the traffic situation is relatively complex by the development of aviation, which brings great challenges to controller. In order to understand the flight situation and provide decision basis for controllers, this paper proposes a key flight conflict nodes identification method based on complex network theory and Analytic Hierarchy Process (AHP)-entropy weight method. Firstly, an aircraft state network is established with aircraft as nodes and Airborne Collision Avoidance System (ACAS) communication relations as connecting edges. On this basis, four parameters, node degree, node weight, clustering coefficient and betweenness, are selected as evaluation indexes of node importance, and the weight of each index is determined by using AHP. And entropy weight method is introduced to revise the results. Node importance is calculated through multi-attribute decision-making method to determine key conflict aircrafts. The simulation and experiment on the artificial network and the aircraft state network of a certain day in the terminal area of Kunming Changshui Airport show that the method proposed in this paper can identify the key flight conflict nodes in the aircraft state network, allocate the selected node deployment can effectively reduce the complexity of the aircraft state network, can provide reference for air traffic control services (ATCS), and reduce the allocation difficulty of controller.

Exploring the robustness of urban bus network: A case from Southern China

The robustness of urban bus network is essential to a city that heavily relies on buses as its main transportation solution. In this paper, the urban bus network has been modeled as a directed and space L network, and Changsha, a transportation hub of nearly 8 million people and hundreds of bus lines in southern China, is taken as a case. Based on the quantitative analyses of the topological properties, it is found that Changsha urban bus network is a scale-free network, not a small-world network. To evaluate the robustness of the network, five scenarios of network failure are simulated, including a random failure and four types of intentional attacks that differed in key node identification methods (i.e., unweighted degree or betweenness centrality) and attack strategies (i.e., normal or cascading attack). It is revealed that intentional attacks are more destructive than a random failure, and cascading attacks are more disruptive than normal attacks in the urban bus network. In addition, the key nodes identification methods are found to play a critical role in the robustness of the urban bus network. Specifically, cascading attack could be more disruptive when the betweenness centrality is used to identify key nodes; in contrast, normal attack could be more disruptive when the unweighted degree is used to identify key nodes. Our results could provide reference for risk management of urban bus network.

中-老交通走廊核心区生态廊道构建与关键节点识别

中-老交通走廊核心区生态廊道构建与关键节点识别

Cross-Face Centrality: A New Measure for Identifying Key Nodes in Networks Based on Formal Concept Analysis

Discovering influential nodes (or actors) in the network is often the key task of mining, analyzing, and understanding real-life networks. Centrality measures are commonly used to detect important nodes that control the information propagation in the network. While off-the-shelf centrality indices may provide effective node identification in several situations, they frequently produce inadequate results when confronted with massive networks, in the presence of complex local structures or the lack of certain characteristics. In this paper, we introduce Cross-face, a new scalable centrality measurement for the identification of key nodes in such networks. Inspired by the Formal Concept Analysis (FCA) framework, the conceptual idea of “Cross-face” is to leverage the faces of concepts to identify nodes that are located in “face bridges” and have an influential “cross clique” connectivity. Thus, it concurrently measures how the node influences its neighbour nodes through its cross cliques while linking the densely connected substructures of the network via its presence in bridges. Unlike traditional centrality measures, the cross-face of nodes can be computed using only a set of symmetrical concepts, which is often quite small compared to the set of nodes or edges in the network. Our experiments on several real-world networks show the efficiency of Cross-face over existing prominent centrality indices such as betweenness, closeness, eigenvector, and k-shell among others.

Identifying Key Nodes in Complex Networks Based on Global Structure

Quantitative identification of key nodes in complex networks is of great significance for studying the robustness and vulnerability of complex networks. Although various centralities have been proposed to solve this issue, each approach has its limitations for its own perspective of determining an actor to be “key”. In this paper, we propose a novel method to identify key nodes in complex networks based on global structure. Three aspects including the shortest path length, the number of shortest paths and the number of non-shortest paths are considered, and we establish three corresponding influence matrices. Node efficiency, which can reflect the contribution of one node to the information transmission of the entire network, is selected as the initial value of node's influence on other nodes, and then the comprehensive influence matrix is constructed to reflect the influence among nodes. The proposed method provides a new measure to identify key nodes in complex networks from the perspective of global network structure, and can obtain more accurate identification results. Four experiments are conducted to evaluate the performance of our proposed method based on Susceptible-Infected (SI) model, and the results demonstrate the superiority of our method.

Identification of key nodes and vital edges in aviation network based on minimum connected dominating set

Identification of key nodes and vital edges are of great importance in aviation network. On the basis of the minimum connected dominating set (MCDS), a backbone network identification method in aviation network was proposed. Key nodes and vital edges are combined as core backbone subnet. The binary particle swarm optimization (BPSO) algorithm is adopted to solve the MCDS problem. Immune mechanism was introduced to guide the search direction of particle nodes to improve the convergence speed of the algorithm. The experiments on artificial network, China airport aviation network and East China airline network show that method is more comprehensive and accurate than other method based on single metric (e.g. degree and closeness centrality methods). And identification results are in good agreement with the actual situation and has high application value in aviation network.

Identification of Key Conflict Nodes Based on Complex Network Theory

The current methods of detecting flight conflicts are difficult to grasp the overall conflict situation in the air, which is not conducive to the controllers to make accurate judgment on different conflict situations. In this paper, a method to identify key nodes of flight conflict based on complex network theory is proposed. Firstly, a conflict situation network model was built based on the ACAS protection area model. Then node degree centrality, closeness centrality and PageRank were used to evaluate the comprehensive importance of all nodes in the conflict situation network by AHP method. And we can find out the key conflict locations and conflict aircrafts based on the evaluation results of the index. The simulation results show that the model can effectively identify the key conflict aircrafts and positions in the airspace. And It can help controllers fully grasp the air security situation by classifing the air conflict situation levels.

Identification of Key Nodes Considering Different Roles of Power Businesses in Power Communication System and Power System

In the power communication network, the importance of the power businesses are the key to evaluate the key nodes of the power communication system. As a special network of power system, when determining the importance of power businesses, the different roles of power businesses in power communication system and power system should be considered. Based on analytic hierarchy process (AHP), entropy weight method and weight degradation method, this paper presents a method to identify the key nodes of power communication network. Firstly, the roles of power businesses in power communication network and power system are analyzed, and a multi-level evaluation model is set up. The evaluation indexes are quantified in the form of quantitative assignment, and the importance of power businesses are evaluated based on improved AHP and entropy weight method. Secondly, on the premise of getting the importance of power businesses, combining with the topology of power communication network to improve the weight degradation method to identify the key nodes of power communication system. Finally, the power communication backbone network of Qingdao and Jinan in China are taken as the simulation objects to verify the correctness of the method.

Approximating Betweenness Centrality to Identify Key Nodes in a Weighted Urban Complex Transportation Network

The key nodes in a complex transportation network have a significant influence on the safety of traffic operations, connectivity reliability, and the performance of the entire network. However, the identification of key nodes in existing urban transportation networks has mainly focused on nonweighted networks and the network information of the nodes themselves, which do not accurately reflect their global status. Thus, the present study proposes a key node identification algorithm that combines traffic flow features and is based on weighted betweenness centrality. This study also uses weighted roads to construct an L-space weighted transportation network and an approximate algorithm for betweenness centrality in order to reduce the complexity of the calculations. The results of the simulation indicate that the proposed algorithm is not only capable of identifying the key nodes in a relatively short amount of time, but it does so with high accuracy. The findings of this study can be used to provide decision-making support for road network management, planning, and urban traffic construction optimization.

Method of key node identification in command and control networks based on level flow betweenness

Key node identification in command and control (C2) networks is an appealing problem that has attracted increasing attention. Due to the particular nature of C2 networks, the traditional algorithms for key nodes identification have problems with high complexity and unsatisfactory adaptability. A new method of key node identification based on level flow betweenness (LFB) is proposed, which is suitable for C2 networks. The proposed method first proves the definition of LFB by analysing the characteristics of a C2 network. Then, this method designs algorithms for key node identification based on LFB and theoretically derives the complexity of this algorithm. Finally, a number of numerical simulation experiments are carried out, and the results demonstrate that this method reduces algorithm complexity, improves identification accuracy and enhances adaptability for C2 networks.

Identification of Key Nodes in Aircraft State Network Based on Complex Network Theory

With the development of aviation, the air traffic density in the terminal area is high and the traffic situation is relatively complex, which brings challenges to the flight deployment. In order to fully understand the air flight situation and provide decision-making basis for controllers, this paper proposes a key conflict aircraft identification method based on complex network theory and node deletion method. First, an aircraft state network is constructed with an aircraft as nodes and airborne collision avoidance system (ACAS) communication relations as edges. Network efficiency, network robustness, connection density, and largest component were used as the indexes of network performance. The weight of each index is determined by using AHP-entropy weight method. A multi-attribute decision-making method was introduced to quantify network performance. Then we used a node deletion method to determine key conflict aircrafts. The simulation and experiment are respectively carried out on the artificial network and the aircraft state network of a certain day in the terminal area of Kunming Changshui Airport. The results show that the method proposed in this paper can identify the key conflict points in the aircraft state network. The deployment of selected nodes can not only effectively reduce the complexity of the flight state network, but also provide a reference for air traffic control services and reduce the control difficulty of the controller.