Dynamic community detection based on the Matthew effect

Sun, Zejun; Sun, Yang; Chang, Xinfeng; Wang, Feifei; Pan, Zhengqiang; Wang, Guan; Liu, Jianfen

doi:10.1016/j.physa.2022.127315

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Specifcally, the algorithm performs static clustering of snapshots at time t and t − 1 and requires that the community structure at time t − 1 remains the same to that at time t as much as possible [21]. Te incremental clusteringbased community detection algorithm takes the community division results of the last moment as the initial condition of the community division at the current moment and carries out incremental updating based on the community division result of the last moment [22]. Tis method updates community changes adaptively.…”

Section: Community Detectionmentioning

confidence: 99%

“…Countries with more international connections with Chinese airports include Tailand (218), Russia (123), Vietnam (75), Malaysia (66), Indonesia (62), the Philippines (47), Singapore (41), India (20), the UAE (19), Laos (15), Myanmar (14), Saudi Arabia (14), Kazakhstan (12), Maldives (12), and Mongolia (10). From a regional perspective, the international route conditions were as follows: Southeast Asia (564), Northeast Asia (133), West Asia and North Africa (66), South Asia (37), Central Asia (22), and Central and Eastern Europe (12). Based on community structure, 22 Chinese hub airports have 582 international air routes, and 29.3% of Chinese airports have 69.8% international routes.…”

Section: Deep Integration Idea Based On Communities' Key Nodementioning

confidence: 99%

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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: Community Detectionmentioning

confidence: 99%

Section: Deep Integration Idea Based On Communities' Key Nodementioning

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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“…In recent years, scholars have paid considerable attention to the study of complex networks [1][2][3][4] , and many real-world relationships and problems can be represented as networks [5][6][7][8] , such as biological networks 9 , power networks 10 , epidemic prevention and control networks 11 , etc. As needed, the abstracted networks can be studied to find solutions to various problems.…”

Section: Introductionmentioning

confidence: 99%

Influential Node Identification Method Based on Multi-Order Neighbors and Exclusive Neighborhood

Wang,

Sun,

Wang

et al. 2023

Preprint

Self Cite

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In a complex network, identifying the influence of nodes and locating the key nodes are helpful for analyzing the network structure and determining the position of nodes to control information transmission, redistribute resources, and regulate the network. We propose an influential node identification method based on multi-order neighbors and exclusive neighborhood (MNEN) based on an analysis and investigation of the existing methods for identifying influential nodes. In the MNEN method, a node's influence is determined by two factors: the node itself, its neighboring nodes, and its exclusive neighborhood. The influence of the node itself is calculated based on its degree value and Ks value (the Ks value is obtained using the K-shell method), while the influence contribution of the neighbor node is calculated based on its degree value, Ks value, and the contribution provided by its exclusive neighbor node. To analyze the performance of the algorithm, we use the SIR model as the evaluation basis, conduct simulation experiments to validate the MNEN method, and compare the experimental results to those of other influential node identification methods. The analysis results indicate that the algorithm accurately identifies influential nodes in networks of varying scales, has a positive overall effect, and is somewhat universal.

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State-of-the-Art in Community Detection in Temporal Networks

Christopoulos

Tsichlas²

2022

IFIP Advances in Information and Communication Technology

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Dynamic community detection based on the Matthew effect

Cited by 7 publications

References 22 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

Influential Node Identification Method Based on Multi-Order Neighbors and Exclusive Neighborhood

State-of-the-Art in Community Detection in Temporal Networks

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