Multi-index algorithm of identifying important nodes in complex networks based on linear discriminant analysis

Hu, Fang; Liu, Yuhua

doi:10.1142/s0217984914502686

Cited by 22 publications

(9 citation statements)

References 30 publications

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“…Figure 3 reports the speed-ups for a variable number of threads. We find that speed-ups scale-up sublinearly for a small number of threads (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), but quickly reach a limit, as induced by the available number of processing units (40 in our case) and overhead for merging the parallel sub-results. Overall, parallelization offers constant speed-up at best, i.e.…”

Section: E Experimental Setupmentioning

confidence: 82%

“…The exact ranking of nodes from higher exact betweenness to lower exact betweenness is R1 = [8,18,13,17,4,10,7,6,15,9,5,2,19,16,3,14,1,12,11,20]. If we use RAND1_4 (sampling four pivots), we can get an estimated ranking R2 = [8,4,17,18,10,7,6,9,13,5,15,14,19,3,2,12,1,11,16,20]. If we only consider Top-1%-Hits (only one node in this small network), this method can get 100% accuracy as it correctly identify Node 8.…”

Section: Measures For Estimating the Quality Of Betweenness Approxmentioning

confidence: 99%

“…Intuitively, the betweenness centrality measures the relevance of a node for transportation of information, goods, or other flows through the network. Empirical studies have used betweenness centrality to analyze protein-protein interaction, to evaluate traffic in communication networks, to evaluate the importance of a node [12], and to identify hubs in transportation networks [13]. Moreover, recent research suggests that betweenness centrality is of great importance for studying the robustness of networks [14], and spreading phenomena.…”

Section: Introductionmentioning

confidence: 99%

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Scalability of Betweenness Approximation Algorithms: An Experimental Review

2019

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Betweenness centrality, which measures the contribution of an individual node to the network's connectivity by counting the number of shortest paths a node appears in, is widely used for the analysis of the complex networks. The computation of exact betweenness centrality is prohibitively expensive for large networks, given a worst-case complexity of O(N * E), where N is the number of nodes and E is the number of edges in the network. Accordingly, a multitude of approximation algorithms has been proposed in the literature. Obtaining an overview of the state of the art is difficult, given a combination of numerous algorithms, parameters, and network topologies. In this paper, we report on the results of the probably largest benchmark performed in this field. Specifically, we select 100 networks with distinct topologies and scales, covering various domains. We devise and compare eight selected measures to evaluate the accuracy of the approximation, compared with the exact betweenness computation. All experiments, including those to obtain the exact betweenness values, have been performed on one computer using a single thread, in order to provide a fair comparison. We implemented typical approximation methods and report sensitivity analysis results with a variety of parameters. We find that a uniformly random sampling method, one of the earliest proposed methods in this field, still delivers the best performance, nicely addressing a sweet spot between quality and runtime complexity. In addition, we carried out robustness experiments based on the ranking order of approximated betweenness, in order to show the effect of different approximations on a realworld task. Our study aims at being a reference for choosing a betweenness approximation method, with consideration of network type, the required level of accuracy, and available computational resources.

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Section: E Experimental Setupmentioning

confidence: 82%

Section: Measures For Estimating the Quality Of Betweenness Approxmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Scalability of Betweenness Approximation Algorithms: An Experimental Review

2019

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“…In complex networks, researchers always identify the important nodes using several evaluation metrics [37] . The representative metrics includes degree centrality (BC), closeness centrality (CC), betweenness centrality (BC) [38] , eigenvector centrality (EC) [39] , current flow closeness centrality (CCC) [40] , and load centrality (LC) [41] .…”

Section: Diagnosis and Treatment Analysis Model Designmentioning

confidence: 99%

An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion

Huang

Sun

et al. 2021

Information Fusion

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Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.

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“…Based on the shortest path, Zheng et al [5] combined proximity and centrality to find important nodes and found through performance analysis that the method was more effective in finding important nodes. Hu et al [6] designed a multi-index method which integrated the centrality of feature vectors and degree centrality to recognize important nodes and found through simulation experiments that the algorithm was more reasonable and accurate. Wen et al [7] designed a "No Return" method for the importance evaluation of aviation network nodes and found that this method could effectively find potential important nodes with high accuracy after testing it on the aviation networks of China and the United States.…”

Section: Introductionmentioning

confidence: 99%

Research on Recognition Algorithm of important Nodes in Complex Network

Su¹

2020

IJCAI

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It is crucial to identify the important nodes in the network. On the basis of the K-shell algorithm, this study researched the recognition of important nodes in complex networks. The study introduces concepts of edge weight and influence coefficient, the design of an IKS algorithm, and an analysis of its recognition effect in Zachary network and real micro blog network. The results showed that the partition results of the K-shell algorithm were coarse, while the partition results of the IKS algorithm were refined; the IKS algorithm could sort the important nodes accurately on the basis of the K-shell algorithm, and its rationality was higher than that of the closeness centralization and PaperRank algorithm. The partition results in the microblog network also verified the effectiveness of the improved method. The experimental results show that the IKS algorithm is reliable in the important node identification, which is beneficial to the recognition of important nodes in complex network. Povzetek: V študentskem prispevku je opisan študij algoritma za določitev pomembnih vozlišč v kompleksnih omrežjih.

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Multi-index algorithm of identifying important nodes in complex networks based on linear discriminant analysis

Cited by 22 publications

References 30 publications

Scalability of Betweenness Approximation Algorithms: An Experimental Review

Scalability of Betweenness Approximation Algorithms: An Experimental Review

An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion

Research on Recognition Algorithm of important Nodes in Complex Network

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