Identifying vital nodes by Achlioptas process

Qiu, Zhihao; Fan, Tianlong; Li, Ming; Lü, Linyuan

doi:10.1088/1367-2630/abe971

Cited by 20 publications

(7 citation statements)

References 59 publications

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“…Nodes on diferent layers are of diferent importance, and nodes closer to the center are more important. Tis method divides the nodes by layer, with low computation complexity, and is applicable to the analysis of large-scale networks [33]. In addition, to overcome the shortcomings of a single index, a global structure model (GSM) that combines indices of node attributes and its global centrality in the network is proposed, which, however, involves more complex computation processes.…”

Section: Key Node Identifcationmentioning

confidence: 99%

Research on Deep Integration of the “Belt and Road” Aviation Network Based on Community Structure from the Perspective of China

Yao

Xiao

2023

Discrete Dynamics in Nature and Society

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Air transport has broad development potential along the “ Belt and Road” by owing to its fast, direct, and comfortable advantages, and the community structure is an important factor determining the efficiency and service functions of the aviation network. The community detection and community structure analysis of the “Belt and Road” aviation network would help to understand the internal relationship of the “Belt and Road” aviation network spatial structure and better integrated and optimized the network. Based on the data of international routes between 522 airports along the “Belt and Road” in 2019, this paper establishes an aviation network model and calculates the degree, average path, clustering coefficient, and centrality index of the “Belt and Road” aviation network. Through Louvain community detection method, 522 airports in the aviation network were divided into four communities, and the structural characteristics and geographical distribution characteristics of the community network were analyzed. The key node identification model in the community is constructed, the hierarchy of each community network is analyzed using the k-core decomposition method, the core node set of the community network is found, the core boundary node set of four aviation community networks is identified, the core node set and core boundary nodes are analyzed repeatedly, and the key node set of the whole network is obtained. Finally, from the perspective of China, a deep integration scheme between Chinese airports, their communities, and other key node sets was proposed. The research found that there are four associations in the “Belt and Road” aviation network: West Asia and North Africa, Russia, Central and Eastern Europe, and China and Southeast Asia. Chinese and Southeast Asian societies rank third among the four societies in terms of scale. They not only have the largest number of key nodes but also the lowest repetition rate of the key nodes. The international airline links in the “Belt and Road” aviation network are more complex. A total of 118 key nodes were identified, of which 83.1% were in the top 20% of overall network centrality.

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Section: Key Node Identifcationmentioning

confidence: 99%

Research on Deep Integration of the “Belt and Road” Aviation Network Based on Community Structure from the Perspective of China

Yao

Xiao

2023

Discrete Dynamics in Nature and Society

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“…Important nodes are nodes that play an essential role in the organization of network structure or the dynamical behavior of network system [ 28 ]. Previous research has generally used node centrality to quantify node importance.…”

Section: Centrality Indicatorsmentioning

confidence: 99%

Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy

Zhou

Chen

et al. 2022

Entropy

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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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“…By using nodes and edges to intuitively describe the nonlinear and heterogeneous interaction patterns of components composing the complex physical, social, or brain systems, its use soon widened to various fields. For decades, scientists have been dedicated to understanding a network's structural and dynamical attributes (like vital node identification 3,4 , high-order network structural analysis [5][6][7][8] , and percolation theory 9 ) and to utilizing these attributes in specific applications, such as link prediction 10 , natural language processing 11 , and recommender systems [12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Determinable and interpretable network representation for link prediction

Deng

2022

Preprint

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As an intuitive description of complex physical, social, or brain systems, complex networks have fascinated scientists for decades. Recently, to abstract a network's structural and dynamical attributes for utilization, network representation has been one focus, mapping a network or its substructures (like nodes) into a low-dimensional vector space. Since the current methods are mostly based on machine learning, a black box of an input-output data fitting mechanism, generally the space's dimension is indeterminable and its elements are not interpreted. Although massive efforts to cope with this issue have included, for example, automated machine learning by computer scientists and computational theory by mathematics, the root causes still remain unresolved. Given that, from a physical perspective, this article proposes two determinable and interpretable node representation methods. To evaluate their effectiveness and generalization, this article further proposes Adaptive and Interpretable ProbS (AIProbS), a network-based model that can utilize node representations for link prediction. Experimental results showed that the AIProbS can reach state-of-the-art precision beyond baseline models, and by and large it can make a good trade-off with machine learning-based models on precision, determinacy, and interpretability, indicating that physical methods could also play a large role in the study of network representation.

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Identifying vital nodes by Achlioptas process

Cited by 20 publications

References 59 publications

Research on Deep Integration of the “Belt and Road” Aviation Network Based on Community Structure from the Perspective of China

Research on Deep Integration of the “Belt and Road” Aviation Network Based on Community Structure from the Perspective of China

Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy

Determinable and interpretable network representation for link prediction

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