Journal articles: 'Important nodes'

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Schipper, Paul. "Lymph Nodes are Important." World Journal of Surgery 33, no. 4 (February 20, 2009): 785–86. http://dx.doi.org/10.1007/s00268-009-9926-4.

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Xu, Shuang, and Pei Wang. "Identifying important nodes by adaptive LeaderRank." Physica A: Statistical Mechanics and its Applications 469 (March 2017): 654–64. http://dx.doi.org/10.1016/j.physa.2016.11.034.

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Li, Jie, Chunlin Yin, Hao Wang, Jian Wang, and Na Zhao. "Mining Algorithm of Relatively Important Nodes Based on Edge Importance Greedy Strategy." Applied Sciences 12, no. 12 (June 15, 2022): 6099. http://dx.doi.org/10.3390/app12126099.

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Relatively important node mining has always been an essential research topic in complex networks. Existing relatively important node mining algorithms suffer from high time complexity and poor accuracy. Therefore, this paper proposes an algorithm for mining relatively important nodes based on the edge importance greedy strategy (EG). This method considers the importance of the edge to represent the degree of association between two connected nodes. Therefore, the greater the value of the connection between a node and a known important node, the more likely it is to be an important node. If the importance of the edges in an undirected network is measured, a greedy strategy can find important nodes. Compared with other relatively important node mining methods on real network data sets, such as SARS and 9/11, the experimental results show that the EG algorithm excels in both accuracy and applicability, which makes it a competitive algorithm in the mining of important nodes in a network.

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Yang, Yunyun, Gang Xie, and Jun Xie. "Mining Important Nodes in Directed Weighted Complex Networks." Discrete Dynamics in Nature and Society 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/9741824.

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In complex networks, mining important nodes has been a matter of concern by scholars. In recent years, scholars have focused on mining important nodes in undirected unweighted complex networks. But most of the methods are not applicable to directed weighted complex networks. Therefore, this paper proposes a Two-Way-PageRank method based on PageRank for further discussion of mining important nodes in directed weighted complex networks. We have mainly considered the frequency of contact between nodes and the length of time of contact between nodes. We have considered the source of the nodes (in-degree) and the whereabouts of the nodes (out-degree) simultaneously. We have given node important performance indicators. Through numerical examples, we analyze the impact of variation of some parameters on node important performance indicators. Finally, the paper has verified the accuracy and validity of the method through empirical network data.

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Liu, Yongshan, Jianjun Wang, Haitao He, Guoyan Huang, and Weibo Shi. "Identifying important nodes affecting network security in complex networks." International Journal of Distributed Sensor Networks 17, no. 2 (February 2021): 155014772199928. http://dx.doi.org/10.1177/1550147721999285.

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An important node identification algorithm based on an improved structural hole and K-shell decomposition algorithm is proposed to identify important nodes that affect security in complex networks. We consider the global structure of a network and propose a network security evaluation index of important nodes that is free of prior knowledge of network organization based on the degree of nodes and nearest neighborhood information. A node information control ability index is proposed according to the structural hole characteristics of nodes. An algorithm ranks the importance of nodes based on the above two indices and the nodes’ local propagation ability. The influence of nodes on network security and their own propagation ability are analyzed by experiments through the evaluation indices of network efficiency, network maximum connectivity coefficient, and Kendall coefficient. Experimental results show that the proposed algorithm can improve the accuracy of important node identification; this analysis has applications in monitoring network security.

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Wang, Pei. "Statistical Identification of Important Nodes in Biological Systems." Journal of Systems Science and Complexity 34, no. 4 (August 2021): 1454–70. http://dx.doi.org/10.1007/s11424-020-0013-0.

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Chen, Young Long, Yung Chi Chang, and Yu Ling Zeng. "An Opportunistic Large Array Concentric Geographic Routing Algorithm with a Relay Node in Wireless Sensor Networks." Applied Mechanics and Materials 764-765 (May 2015): 838–42. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.838.

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Wireless sensor networks (WSNs) are a group of wireless sensor nodes, those sensor nodes with sensing and monitoring of environmental information. Energy consumption is an important topic; the node's power is limited. Therefore, we proposed an Opportunistic Large Array Concentric Geographic Routing Algorithm (OLACGRA) to reduce the node’s energy consumption and analysis the characteristic of energy model. The sink position of our proposed OLACGRA is at the center of concentric topology architecture. The source node wants to transmit data that it needs to calculate the distance between source node and sink node. If this distance bigger than threshold value, we use the multi-hop manner. Otherwise, source node transmits data to sink node directly. Simulation results show that our proposed algorithm can effectively reduce the node’s energy consumption.

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Sun, Yu, Pei-Yang Yao, Lu-Jun Wan, Jian Shen, and Yun Zhong. "Ranking important nodes in complex networks by simulated annealing." Chinese Physics B 26, no. 2 (February 2017): 020201. http://dx.doi.org/10.1088/1674-1056/26/2/020201.

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Mester, Attila, Andrei Pop, Bogdan-Eduard-Mădălin Mursa, Horea Greblă, Laura Dioşan, and Camelia Chira. "Network Analysis Based on Important Node Selection and Community Detection." Mathematics 9, no. 18 (September 17, 2021): 2294. http://dx.doi.org/10.3390/math9182294.

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The stability and robustness of a complex network can be significantly improved by determining important nodes and by analyzing their tendency to group into clusters. Several centrality measures for evaluating the importance of a node in a complex network exist in the literature, each one focusing on a different perspective. Community detection algorithms can be used to determine clusters of nodes based on the network structure. This paper shows by empirical means that node importance can be evaluated by a dual perspective—by combining the traditional centrality measures regarding the whole network as one unit, and by analyzing the node clusters yielded by community detection. Not only do these approaches offer overlapping results but also complementary information regarding the top important nodes. To confirm this mechanism, we performed experiments for synthetic and real-world networks and the results indicate the interesting relation between important nodes on community and network level.

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Yu, Yong, Biao Zhou, Linjie Chen, Tao Gao, and Jinzhuo Liu. "Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy." Entropy 24, no. 2 (February 14, 2022): 275. http://dx.doi.org/10.3390/e24020275.

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In recent years, the identification of the essential nodes in complex networks has attracted significant attention because of their theoretical and practical significance in many applications, such as preventing and controlling epidemic diseases and discovering essential proteins. Several importance measures have been proposed from diverse perspectives to identify crucial nodes more accurately. In this paper, we propose a novel importance metric called node propagation entropy, which uses a combination of the clustering coefficients of nodes and the influence of the first- and second-order neighbor numbers on node importance to identify essential nodes from an entropy perspective while considering the local and global information of the network. Furthermore, the susceptible–infected–removed and susceptible–infected–removed–susceptible epidemic models along with the Kendall coefficient are used to reveal the relevant correlations among the various importance measures. The results of experiments conducted on several real networks from different domains show that the proposed metric is more accurate and stable in identifying significant nodes than many existing techniques, including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and H-index.

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Xu, Hui, Jianpei Zhang, Jing Yang, and Lijun Lun. "Identifying Important Nodes in Complex Networks Based on Multiattribute Evaluation." Mathematical Problems in Engineering 2018 (May 31, 2018): 1–11. http://dx.doi.org/10.1155/2018/8268436.

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Assessing and measuring the importance of nodes in a complex network are of great theoretical and practical significance to improve the robustness of the actual system and to design an efficient system structure. The classical local centrality measures of important nodes only take the number of node neighbors into consideration but ignore the topological relations and interactions among neighbors. Due to the complexity of the algorithm itself, the global centrality measure cannot be applied to the analysis of large-scale complex network. The k-shell decomposition method considers the core node located in the center of the network as the most important node, but it only considers the residual degree and neglects the interaction and topological structure between the node and its neighbors. In order to identify the important nodes efficiently and accurately in the network, this paper proposes a local centrality measurement method based on the topological structure and interaction characteristics of the nodes and their neighbors. On the basis of the k-shell decomposition method, the method we proposed introduces two properties of structure hole and degree centrality, which synthetically considers the nodes and their neighbors’ network location information, topological structure, scale characteristics, and the interaction between different nuclear layers of them. In this paper, selective attacks on four real networks are, respectively, carried out. We make comparative analyses of the averagely descending ratio of network efficiency between our approach and other seven indices. The experimental results show that our approach is valid and feasible.

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Dai, Yulong, Qiyou Shen, Xiangqian Xu, and Jun Yang. "Identifying important nodes from content-associated heterogeneous graph by LeaderRank." Journal of Physics: Conference Series 2113, no. 1 (November 1, 2021): 012082. http://dx.doi.org/10.1088/1742-6596/2113/1/012082.

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Abstract Most real-world systems consist of a large number of interacting entities of many types. However, most of the current researches on systems are based on the assumption that the type of node or link in the network is unique. In other words, the network is homogeneous, containing the same type of nodes and links. Based on this assumption, differential information between nodes and edges is ignored. This paper firstly introduces the research background, challenges and significance of this research. Secondly, the basic concepts of the model are introduced. Thirdly, a novel type-sensitive LeaderRank algorithm is proposed and combined with distance rule to solve the importance ranking problem of content-associated heterogeneous graph nodes. Finally, the writer influence data set is used for experimental analysis to further prove the validity of the model.

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Wang, Xue-Guang. "Research on Critical Nodes Algorithm in Social Complex Networks." Open Physics 15, no. 1 (March 16, 2017): 68–73. http://dx.doi.org/10.1515/phys-2017-0008.

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AbstractDiscovering critical nodes in social networks has many important applications and has attracted more and more institutions and scholars. How to determine the K critical nodes with the most influence in a social network is a NP (define) problem. Considering the widespread community structure, this paper presents an algorithm for discovering critical nodes based on two information diffusion models and obtains each node’s marginal contribution by using a Monte-Carlo method in social networks. The solution of the critical nodes problem is the K nodes with the highest marginal contributions. The feasibility and effectiveness of our method have been verified on two synthetic datasets and four real datasets.

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Luo, Liang, Minghao Li, Zili Zhang, and Li Tao. "Identifying important nodes based on upstream and downstream time-respecting paths in temporal networks." Modern Physics Letters B 35, no. 23 (July 20, 2021): 2150403. http://dx.doi.org/10.1142/s0217984921504030.

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Identifying the nodes that play significant roles in the epidemic spreading process has attracted extensive attention in recent years. Few centrality measures, such as temporal degree and temporal closeness centrality, have been proposed to quantify node importance based on the topological structure of social contact networks. Most methods estimate the importance of a node from a single aspect, e.g. a higher degree in time snapshot graphs, or shorter distances to other nodes along time-respecting paths. However, this may not be the case in the real world. On the one hand, a node with more nodes on its out streams (i.e. downstream) should be more important because it may affect more nodes along its time-stamped contacting paths once it is infected. On the other hand, a node with more nodes in its in streams (i.e. upstream) deserves closer attention, as it has a higher probability of infection by other nodes. We propose a new temporal centrality measure, upstream and downstream centrality (UD-centrality) with two forms of realizations, i.e. a linear UD-centrality (L-UD) and a product UD-centrality (P-UD) to estimate the importance of nodes based on the temporal structures of social contact networks. We compare our L-UD and P-UD to three classic temporal network centralities through simulations on 14 real-world temporal networks based on the susceptible-infected (SI) model. The comparison results show that UD-centrality can more accurately rank the importance of nodes than the baseline centrality measures.

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Grolmusz, Vince, Gabor Ivan, Daniel Banky, and Balazs Szerencsi. "How to Find Non Hub Important Nodes in Protein Networks?" Biophysical Journal 102, no. 3 (January 2012): 184a. http://dx.doi.org/10.1016/j.bpj.2011.11.1004.

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Zhao, Na, Qian Liu, Ming Jing, Jie Li, Zhidan Zhao, and Jian Wang. "DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion." Applied Sciences 12, no. 1 (January 5, 2022): 522. http://dx.doi.org/10.3390/app12010522.

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In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.

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Yu, Genghua, Zhigang Chen, Jia Wu, and Jian Wu. "A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network." Symmetry 10, no. 11 (November 6, 2018): 600. http://dx.doi.org/10.3390/sym10110600.

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The amount of data has skyrocketed in Fifth-generation (5G) networks. How to select an appropriate node to transmit information is important when we analyze complex data in 5G communication. We could sophisticate decision-making methods for more convenient data transmission, and opportunistic complex social networks play an increasingly important role. Users can adopt it for information sharing and data transmission. However, the encountering of nodes in mobile opportunistic network is random. The latest probabilistic routing method may not consider the social and cooperative nature of nodes, and could not be well applied to the large data transmission problem of social networks. Thus, we quantify the social and cooperative relationships symmetrically between the mobile devices themselves and the nodes, and then propose a routing algorithm based on an improved probability model to predict the probability of encounters between nodes (PEBN). Since our algorithm comprehensively considers the social relationship and cooperation relationship between nodes, the prediction result of the target node can also be given without encountering information. The neighbor nodes with higher probability are filtered by the prediction result. In the experiment, we set the node’s selfishness randomly. The simulation results show that compared with other state-of-art transmission models, our algorithm has significantly improved the message delivery rate, hop count, and overhead.

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Xu, Deng Yuan, and Zhong Wei Hou. "Power Control DSR Routing in Tunnel Environment Monitoring." Advanced Materials Research 524-527 (May 2012): 815–18. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.815.

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As one of important technologies of IOT (Internet of Things), WSN (Wireless Sensor Networks) has been used in tunnel environmental monitoring. Tunnel environment monitoring has its particularity that WSN nodes show linear topologies. Traditional routing algorithms in WSN do not consider the linear topology of sensor nodes in tunnel and are difficult to realize long-time data transmission in limited battery power. In this paper, we propose Power Control Dynamic Source Routing algorithm (PC-DSR) by the thought of cross-layer design. Routing table is established according to the distance between nodes and the residual energy of nodes and optimum transmission power is calculated in order to save nodes’ power and prolong the life-time of the whole networks. Simulation results show that the novel algorithm can save node's transmission power, which increase the WSN lifetime of 12.3%.

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Yang Xiong, Huang De-Cai, and Zhang Zi-Ke. "Recommendation of important nodes in deployment optimization model of defense strategy." Acta Physica Sinica 64, no. 5 (2015): 050502. http://dx.doi.org/10.7498/aps.64.050502.

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Jiang, Jiu-Lei, Hui Fang, Sheng-Qing Li, and Wei-Min Li. "Identifying important nodes for temporal networks based on the ASAM model." Physica A: Statistical Mechanics and its Applications 586 (January 2022): 126455. http://dx.doi.org/10.1016/j.physa.2021.126455.

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Jia, Jianfeng, Xuewei Liu, Yixin Zhang, Zhe Li, Yanjie Xu, and Jiaqi Yan. "Rumor propagation controlling based on finding important nodes in complex network." Journal of Industrial & Management Optimization 16, no. 5 (2020): 2521–29. http://dx.doi.org/10.3934/jimo.2019067.

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Lu, Pengli, and Chen Dong. "EMH: Extended Mixing H-index centrality for identification important users in social networks based on neighborhood diversity." Modern Physics Letters B 34, no. 26 (June 6, 2020): 2050284. http://dx.doi.org/10.1142/s021798492050284x.

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The rapid expansion of social network provides a suitable platform for users to deliver messages. Through the social network, we can harvest resources and share messages in a very short time. The developing of social network has brought us tremendous conveniences. However, nodes that make up the network have different spreading capability, which are constrained by many factors, and the topological structure of network is the principal element. In order to calculate the importance of nodes in network more accurately, this paper defines the improved H-index (IH) centrality according to the diversity of neighboring nodes, then uses the cumulative centrality (MC) to take all neighboring nodes into consideration, and proposes the extended mixing H-index (EMH) centrality. We evaluate the proposed method by Susceptible–Infected–Recovered (SIR) model and monotonicity which are used to assess accuracy and resolution of the method, respectively. Experimental results indicate that the proposed method is superior to the existing measures of identifying nodes in different networks.

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You, Lu, Pan Zhiyuan, and Xu Wei. "Robustness Evaluation Strategy of Ubiquitous Power Internet of Things Based on Important Node Recognition." E3S Web of Conferences 136 (2019): 01011. http://dx.doi.org/10.1051/e3sconf/201913601011.

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This paper analyses the structure and characteristics of ubiquitous power Internet of things (UP-IoT) from four levels: the perception layer, network layer, platform layer and application layer. The robustness of UP-IoT is defined from the perspective of system structure, and the internal and external disturbance factors of robustness are analysed. According to the scale-free characteristics of complex network, a robustness evaluation strategy for UP-IoT based on identification of important nodes is proposed. A set of robustness evaluation indexes, including degree centrality, betweenness centrality, closeness centrality, maximum connectivity and connectivity factors, are established to quantify the importance of nodes. The model in this paper is used to analyse the UP-IoT network model with 12 nodes and verify the feasibility of the evaluation strategy.

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Chen, Xuegong, Jie Zhou, Zhifang Liao, Shengzong Liu, and Yan Zhang. "A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy." Entropy 22, no. 8 (July 31, 2020): 848. http://dx.doi.org/10.3390/e22080848.

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With the rapid development of social networks, it has become extremely important to evaluate the propagation capabilities of the nodes in a network. Related research has wide applications, such as in network monitoring and rumor control. However, the current research on the propagation ability of network nodes is mostly based on the analysis of the degree of nodes. The method is simple, but the effectiveness needs to be improved. Based on this problem, this paper proposes a method that is based on Tsallis entropy to detect the propagation ability of network nodes. This method comprehensively considers the relationship between a node’s Tsallis entropy and its neighbors, employs the Tsallis entropy method to construct the TsallisRank algorithm, and uses the SIR (Susceptible, Infectious, Recovered) model for verifying the correctness of the algorithm. The experimental results show that, in a real network, this method can effectively and accurately evaluate the propagation ability of network nodes.

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Congdong, Li, Yang Weiming, Yu Yinyun, and Li Bingjun. "Research on node importance evaluation of complex products based on three-parameter interval grey number grey relational model." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 1931–48. http://dx.doi.org/10.3233/jifs-210635.

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In the process of product development, the identification and evaluation of important nodes is of great significance for the effective control of complex product engineering change. In order to identify and evaluate important nodes accurately, this paper proposes a method to evaluate the importance of complex product nodes. Firstly, an engineering change expression method based on multi-stage complex network is proposed. Then, the evaluation index system of important nodes of complex products is constructed. A three parameter grey relational model based on subjective and objective weights is proposed to identify and evaluate the important nodes of complex products. Finally, an example of a large permanent magnet synchronous centrifugal compressor is analyzed. The example shows that the top nodes are node 4, 1, 7, 9 and 24. Compared with other experiments, the proposed method can effectively and reasonably evaluate the node importance of complex products.

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Chen, Qian, and Thomas D. Pollard. "Actin filament severing by cofilin is more important for assembly than constriction of the cytokinetic contractile ring." Journal of Cell Biology 195, no. 3 (October 24, 2011): 485–98. http://dx.doi.org/10.1083/jcb.201103067.

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We created two new mutants of fission yeast cofilin to investigate why cytokinesis in many organisms depends on this small actin-binding protein. These mutant cofilins bound actin monomers normally, but bound and severed ADP-actin filaments much slower than wild-type cofilin. Cells depending on mutant cofilins condensed nodes, precursors of the contractile ring, into clumps rather than rings. Starting from clumped nodes, mutant cells slowly assembled rings from diverse intermediate structures including spiral strands containing actin filaments and other contractile ring proteins. This process in mutant cells depended on α-actinin. These slowly assembled contractile rings constricted at a normal rate but with more variability, indicating ring constriction is not very sensitive to defects in severing by cofilin. Computer simulations of the search-capture-pull and release model of contractile ring formation predicted that nodes clump when the release step is slow, so cofilin severing of actin filament connections between nodes likely contributes to the release step.

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Sah, Omprakash, Shivam Bathla, and Anurag Singh. "SHOMAN: An Efficient Method for Finding the Important Nodes in a Network." International Journal of Business Intelligence and Data Mining 19, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijbidm.2021.10036357.

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Bathla, Shivam, Omprakash Sah Kanu, and Anurag Singh. "SHOMAN: an efficient method for finding the important nodes in a network." International Journal of Business Intelligence and Data Mining 19, no. 3 (2021): 291. http://dx.doi.org/10.1504/ijbidm.2021.118205.

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Ol'gina, I. G. "METHOD FOR IDENTIFYING IMPORTANT NODES IN THE CITATION NETWORK OF SCIENTIFIC PUBLICATIONS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 203 (May 2021): 3–10. http://dx.doi.org/10.14489/vkit.2021.05.pp.003-010.

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A mathematical method for selecting and ranking publications according to the degree of their compliance with the objectives of the research is developed and investigated. Bibliographic and abstract databases are used as primary sources of data on publications, which make it possible to track the citation of publications and identify the corresponding citation networks. The subject of the study is the citation networks of scientific publications. The mathematical model of citation networks is simple directed graphs whose vertices correspond to publications and whose arcs correspond to bibliographic references. The objectives of the research can be the preparation of a scientific article, writing a monograph or textbook, the design of a final qualifying work or dissertation, the replenishment of the library fund, etc. The research is carried out using the methods of Network Science. A method is proposed for determining the importance of the nodes of the citation network of scientific publications, taking into account the relevant measures of the centrality of the nodes and the profile of the research of publications. Relevant measures of centrality are indicators of the importance of nodes that are more appropriate than others and meet the search query for the selection of publications. The paper considers three profiles of the research of the citation network in order to rank publications. Since the citation network is oriented, the incoming and outgoing connections of the node are analyzed separately. The difference between the study profiles is that in one of them, only outgoing connections are taken into account in the centrality measures, in the other – incoming connections, and in the next, both are taken into account. An example of the application of the developed method of selection and ranking of scientific publications is given. The experimental values of the network node importance indicators were obtained on the basis of data on the citation of scientific publications in the RePEc bibliographic database.

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Shon, Jin Gon, Yong-Hwan Kim, and Youn-Hee Han. "Local Information-based Betweenness Centrality to Identify Important Nodes in Social Networks." KIPS Transactions on Computer and Communication Systems 2, no. 5 (May 31, 2013): 209–16. http://dx.doi.org/10.3745/ktccs.2013.2.5.209.

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Neville, A. Munro. "Breast cancer micrometastases in lymph nodes and bone marrow are prognostically important." Annals of Oncology 2, no. 1 (January 1991): 13–14. http://dx.doi.org/10.1093/oxfordjournals.annonc.a057812.

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Son, Taeil, Woo Jin Hyung, Jong Won Kim, Hyoung-Il Kim, Ji Yeong An, Jae-Ho Cheong, Seung Ho Choi, and Sung Hoon Noh. "Anatomic Extent of Metastatic Lymph Nodes: Still Important for Gastric Cancer Prognosis." Annals of Surgical Oncology 21, no. 3 (November 26, 2013): 899–907. http://dx.doi.org/10.1245/s10434-013-3403-x.

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Shun, Chen, Zhao Xudong, Xiao Baoqiang, Xiao Yuanhao, and Xi Junpeng. "Evaluation Method Of Important Nodes Of Water Supply Network Under Terrorist Attack." IOP Conference Series: Earth and Environmental Science 571 (November 26, 2020): 012095. http://dx.doi.org/10.1088/1755-1315/571/1/012095.

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Zhang, Hong, Changzhen Hu, and Xiaojun Wang. "Brittleness analysis and important nodes discovery in large time-evolving complex networks." Journal of Shanghai Jiaotong University (Science) 22, no. 1 (January 26, 2017): 50–54. http://dx.doi.org/10.1007/s12204-017-1798-4.

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Wang, Pei, Jinhu Lü, and Xinghuo Yu. "Identification of Important Nodes in Directed Biological Networks: A Network Motif Approach." PLoS ONE 9, no. 8 (August 29, 2014): e106132. http://dx.doi.org/10.1371/journal.pone.0106132.

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Nieman, Dylan R., Christian G. Peyre, Thomas J. Watson, Wenqing Cao, Michael D. Lunt, Michal J. Lada, Michelle S. Han, Carolyn E. Jones, and Jeffrey H. Peters. "Neoadjuvant Treatment Response in Negative Nodes Is an Important Prognosticator After Esophagectomy." Annals of Thoracic Surgery 99, no. 1 (January 2015): 277–83. http://dx.doi.org/10.1016/j.athoracsur.2014.07.037.

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Xu, Qing, Tianming Zhao, Xinming Zhu, and Ruoxu Chen. "K-shell Algorithm-based Method for Identifying Important Nodes in Rescue Paths." Sensors and Materials 34, no. 11 (November 16, 2022): 4039. http://dx.doi.org/10.18494/sam4063.

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Li, Hanwen, and Yong Deng. "Local volume dimension: A novel approach for important nodes identification in complex networks." International Journal of Modern Physics B 35, no. 05 (February 19, 2021): 2150069. http://dx.doi.org/10.1142/s0217979221500697.

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How to identify important nodes in complex networks? It is still an open problem. Many methods have been proposed to tackle this problem. The main contribution of this paper is to propose a method to identify important nodes based on local volume dimension (LVD). If the LVD of the node is lower, the node is more important. Promising results of experiments on four real-world networks compared with six methods under both Susceptible–Infected (SI) model and Susceptible–Infected–Recovered (SIR) model validate and demonstrate the effectiveness of the proposed method.

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Li, Xuequn, Shuming Zhou, Jiafei Liu, Gaolin Chen, Zhendong Gu, and Yihong Wang. "A new metric to quantify influence of nodes in social networks." International Journal of Modern Physics B 33, no. 17 (July 10, 2019): 1950186. http://dx.doi.org/10.1142/s0217979219501868.

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Reasonably ranking the influence of nodes in social networks is increasingly important not only for theoretical research but also for real applications. A great number of strategies to identify the influence of nodes have been proposed so far, such as semi-local centrality (SL), betweenness centrality and coreness centrality, etc. For the sake of ranking more effectively, a new method of identifying influential nodes is proposed in this paper, which takes into account a node’s influence on its neighbors and the node’s position in the network. The influence on neighbors involves two aspects. One is the influence of the target node on its direct neighbors (h-index), the other is the influence on farther neighbors (semi-local centrality). The location of the node in the network is reflected by the improved k-core score, a modified version of k-core index to make it more applicable to practice. Combining both local and global information of node together makes the proposed method a reasonable and effective strategy to identify the influential nodes. The simulation results compared to other well-known methods on six real-world networks demonstrate the effectiveness of the presented method.

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Wang, Xue-Guang. "An Algorithm for Critical Nodes Problem in Social Networks Based on Owen Value." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/414717.

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Discovering critical nodes in social networks has many important applications. For finding out the critical nodes and considering the widespread community structure in social networks, we obtain each node’s marginal contribution by Owen value. And then we can give a method for the solution of the critical node problem. We validate the feasibility and effectiveness of our method on two synthetic datasets and six real datasets. At the same time, the result obtained by using our method to analyze the terrorist network is in line with the actual situation.

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Berberler, Zeynep Nihan, Halil İbrahim Yildirim, Tolga İltüzer, and İzzet Tunç. "Agglomeration-Based Node Importance Analysis in Wheel-Type Networks." International Journal of Foundations of Computer Science 32, no. 03 (January 13, 2021): 269–88. http://dx.doi.org/10.1142/s0129054121500210.

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Evaluating the importance of nodes for complex networks is an important part of invulnerability research. In this paper, node importance analysis is conducted in wheel-related networks by a method of evaluating node importance by node contraction based on network agglomeration in communication networks. Both degrees and positions of nodes are considered with this method. This method was also proved to be feasible and effective measure to identify influential nodes in a network.

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Ng, Geok See, D. Shi, A. Wahab, and H. Singh. "Entropy Learning in Neural Network." ASEAN Journal on Science and Technology for Development 20, no. 3&4 (December 27, 2017): 307–22. http://dx.doi.org/10.29037/ajstd.362.

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In this paper, entropy term is used in the learning phase of a neural network. As learning progresses, more hidden nodes get into saturation. The early creation of such hidden nodes may impair generalisation. Hence entropy approach is proposed to dampen the early creation of such nodes. The entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes. At the end of learning, the less important nodes can then be eliminated to reduce the memory requirements of the neural network.

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Karabekmez, Muhammed Erkan, and Betul Kirdar. "A novel topological centrality measure capturing biologically important proteins." Molecular BioSystems 12, no. 2 (2016): 666–73. http://dx.doi.org/10.1039/c5mb00732a.

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In the present study, a novel metric of centrality—weighted sum of loads eigenvector centrality (WSL-EC)—based on graph spectra is defined and its performance in identifying topologically and biologically important nodes is comparatively investigated with common metrics of centrality in a human protein–protein interaction network.

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Khasawneh, Mahmoud, and Anjali Agarwal. "A Collaborative Approach for Monitoring Nodes Behavior during Spectrum Sensing to Mitigate Multiple Attacks in Cognitive Radio Networks." Security and Communication Networks 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/3261058.

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Spectrum sensing is the first step to overcome the spectrum scarcity problem in Cognitive Radio Networks (CRNs) wherein all unutilized subbands in the radio environment are explored for better spectrum utilization. Adversary nodes can threaten these spectrum sensing results by launching passive and active attacks that prevent legitimate nodes from using the spectrum efficiently. Securing the spectrum sensing process has become an important issue in CRNs in order to ensure reliable and secure spectrum sensing and fair management of resources. In this paper, a novel collaborative approach during spectrum sensing process is proposed. It monitors the behavior of sensing nodes and identifies the malicious and misbehaving sensing nodes. The proposed approach measures the node’s sensing reliability using a value called belief level. All the sensing nodes are grouped into a specific number of clusters. In each cluster, a sensing node is selected as a cluster head that is responsible for collecting sensing-reputation reports from different cognitive nodes about each node in the same cluster. The cluster head analyzes information to monitor and judge the nodes’ behavior. By simulating the proposed approach, we showed its importance and its efficiency for achieving better spectrum security by mitigating multiple passive and active attacks.

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Zeng, Jin, Chenxi Shao, Xingfu Wang, and Fuyou Miao. "Evaluation method for node importance based on attraction between nodes." International Journal of Modern Physics C 29, no. 12 (December 2018): 1850125. http://dx.doi.org/10.1142/s0129183118501255.

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Vital node, which has some special functions, plays an important role compared to other nodes in complex networks. Recently, the discovery of vital nodes in complex networks has captured increasing attention due to their important theoretical significance and great practicability. By defining the confidence of the node and the inter-node attraction, the significance of the node is measured by the product of the confidence of the node and the aggregation of attractions of the node on other nodes in the network. The experimental results illustrate that the proposed method has higher precision and performs well on various networks with different structures.

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Wu, Yina, and Hui Ma. "Logistics Network Nodes Importance Analysis Based on the Complex Network Theory." Applied Mechanics and Materials 336-338 (July 2013): 2410–14. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2410.

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Logistics systems can be abstracted to complex networks which are composed of logistics nodes and transport routes. The structure and geometric properties of the complex network has an important impact on the logistics industry development and management. The article use a provinces logistics network as the prototype and build a complex network. Apply complex network theory to analyze the logistics network. The article found that the logistics network has small-world properties. Also, the article discussed the important nodes based on the statistical indictors. Finally, compare the results with the real planning nodes level.

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Bai, Yu, Ding-Ding Han, and Ming Tang. "Multi-priority routing algorithm based on source node importance in complex networks." International Journal of Modern Physics C 30, no. 07 (July 2019): 1940010. http://dx.doi.org/10.1142/s0129183119400102.

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Network transmission capacity is an important guarantee for the normal operation of the network. The effective routing strategy avoids the use of nodes with large degree value, which leads to low utilization of nodes and failure to consider the priorities of different packets. On this basis, a routing algorithm based on packet source node classification is proposed. This strategy introduces an adjustable parameter. By adjusting this parameter, the data packets generated at the important nodes are transferred to the nodes with higher degree, which is to say they can reach the destination faster. The data packets generated at the sub-important nodes are transmitted by nodes with smaller degrees, thus reaching the destination relatively slowly. The routing strategy is evaluated in terms of order parameters, average routing time and node utilization. Compared with nonclassified routing and randomly classified routing strategy, the network transmission capacity was increased by 19% and 38%, respectively. Each node in the network was used more evenly. At the same time, the network transmission capacity under different parameters is analyzed theoretically through a series of derivations. In order to explore the performance of routing strategy in actual networks, this paper selects the actual network of web-EPA for simulation. The experimental results show that the proposed routing strategy is 7% and 17% higher than the nonclassified routing and random classified routing, respectively.

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Trouli, Georgia Eirini, Alexandros Pappas, Georgia Troullinou, Lefteris Koumakis, Nikos Papadakis, and Haridimos Kondylakis. "SumMER: Structural Summarization for RDF/S KGs." Algorithms 16, no. 1 (December 27, 2022): 18. http://dx.doi.org/10.3390/a16010018.

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Knowledge graphs are becoming more and more prevalent on the web, ranging from small taxonomies, to large knowledge bases containing a vast amount of information. To construct such knowledge graphs either automatically or manually, tools are necessary for their quick exploration and understanding. Semantic summaries have been proposed as a key technology enabling the quick understanding and exploration of large knowledge graphs. Among the methods proposed for generating summaries, structural methods exploit primarily the structure of the graph in order to generate the result summaries. Approaches in the area focus on identifying the most important nodes and usually employ a single centrality measure, capturing a specific perspective on the notion of a node’s importance. Moving from one centrality measure to many however, has the potential to generate a more objective view on nodes’ importance, leading to better summaries. In this paper, we present SumMER, the first structural summarization technique exploiting machine learning techniques for RDF/S KGs. SumMER explores eight centrality measures and then exploits machine learning techniques for optimally selecting the most important nodes. Then those nodes are linked formulating a subgraph out of the original graph. We experimentally show that combining centrality measures with machine learning effectively increases the quality of the generated summaries.

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NG, GEOK SEE, ABDUL WAHAB, and DAMING SHI. "ENTROPY LEARNING AND RELEVANCE CRITERIA FOR NEURAL NETWORK PRUNING." International Journal of Neural Systems 13, no. 05 (October 2003): 291–305. http://dx.doi.org/10.1142/s0129065703001637.

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In this paper, entropy is a term used in the learning phase of a neural network. As learning progresses, more hidden nodes get into saturation. The early creation of such hidden nodes may impair generalisation. Hence an entropy approach is proposed to dampen the early creation of such nodes by using a new computation called entropy cycle. Entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes. At the end of learning, the less important nodes can then be pruned to reduce the memory requirements of the neural network.

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Tuma, Rabiya S. "For Breast Cancer Prognosis, Number of Nodes Not as Important as Type; For Early Disease, Preop Detection of Positive Nodes Effective." Oncology Times 31, no. 23 (December 2009): 25–26. http://dx.doi.org/10.1097/01.cot.0000365567.33554.56.

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Journal articles on the topic 'Important nodes'

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