The random walk-based gravity model to identify influential nodes in complex networks

Zhao, Jie; Wen, Tao; Jahanshahi, Hadi; Cheong, Kang Hao

doi:10.1016/j.ins.2022.07.084

Cited by 49 publications

(4 citation statements)

References 44 publications

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“…Future research can correct the gravity model by evaluating the comprehensive strength of the city using a multiindicator evaluation system (Zhang et al, 2022). Second, in the process of measuring the structural characteristics of urban networks, betweenness centrality cannot apply to large networks because of the high time complexity (Zhao et al, 2022). Therefore, future research could look for more effective ways to identify influential nodes in networks of economic linkages.…”

Section: Recommendationsmentioning

confidence: 99%

Understanding the structure and determinants of economic linkage network: The case of three major city clusters in Yangtze River Economic belt

Zhang

Sheng

2023

Front. Environ. Sci.

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The economic linkages between cities within the Yangtze River Economic Belt city cluster are important for precisely implementing the Yangtze River Economic Belt strategy. Accordingly, this study used the revised gravity model, social network method, and spatial Durbin model to analyze the economic linkage strength, spatial-temporal differentiation, and driving factors of the three major city cluster in the Yangtze River Economic Belt from 2006–2020. The results confirm that the Chengdu-Chongqing urban agglomeration has formed a network structure with Chengdu and Chongqing as the two poles; the urban agglomeration in the middle reaches of the Yangtze River has formed a point-axis development pattern of Wuhan, Changsha and Nanchang. The Yangtze River Delta urban agglomeration formed a multi-center radiation and sub-network development pattern. Additionally, the characteristics of network structure significantly affected the flow of factors, the level of economic development was the basis for strengthening communication and cooperation between cities. Meanwhile, the advanced industrial structures played an important role in promoting industrial linkages, and human capital exerts an obvious spatial spillover effect. Therefore, these three major urban agglomerations should implement differentiated development strategies and enhance the synergy of their linkage networks, thus promoting high-quality development.

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Section: Recommendationsmentioning

confidence: 99%

Understanding the structure and determinants of economic linkage network: The case of three major city clusters in Yangtze River Economic belt

Zhang

Sheng

2023

Front. Environ. Sci.

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“…This kind of means has certain guiding significance, but it ignores the correlation between the selected attributes. Recently, inspired by the gravity formulation, researchers have proposed gravity model centrality [26] and some extension methods [27][28][29] to identify influencers in complex networks. These approaches, with excellent performance, combine the local and path information.…”

Section: J Stat Mech (2023) 083402mentioning

confidence: 99%

Identifying influential nodes in complex networks based on network embedding and local structure entropy

Lu,

Yang,

Zhang

2023

J. Stat. Mech.

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The identification of influential nodes in complex networks remains a crucial research direction, as it paves the way for analyzing and controlling information diffusion. The currently presented network embedding algorithms are capable of representing high-dimensional and sparse networks with low-dimensional and dense vector spaces, which not only keeps the network structure but also has high accuracy. In this work, a novel centrality approach based on network embedding and local structure entropy, called the ELSEC, is proposed for capturing richer information to evaluate the importance of nodes from the view of local and global perspectives. In short, firstly, the local structure entropy is used to measure the self importance of nodes. Secondly, the network is mapped to a vector space to calculate the Manhattan distance between nodes by using the Node2vec network embedding algorithm, and the global importance of nodes is defined by combining the correlation coefficients. To reveal the effectiveness of the ELSEC, we select three types of algorithms for identifying key nodes as contrast approaches, including methods based on node centrality, optimal decycling based algorithms and graph partition based methods, and conduct experiments on ten real networks for correlation, ranking monotonicity, accuracy of high ranking nodes and the size of the giant connected component. Experimental results show that the ELSEC algorithm has excellent ability to identify influential nodes.

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“…Random walk is an important method for exploring topological structure and intrinsic character of networks and has received widespread attention [1][2][3][4][5][6][7]. The main research interests include the first passage time of ordinary random walks [8][9][10], the navigation rules of biased random walks [11,12], the long-range properties of self-avoiding random walks [13], the random walk with memory [14,15], etc.…”

Section: Introductionmentioning

confidence: 99%

Quantitative study of random walk parameters in node2vec model

Li,

Yang

2024

Phys. Scr.

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In this paper, the Monte Carlo simulation method is used to investigate a generalized random walk model based on node2vec which is a popular algorithm in network embedding and has been widely applied in various domains such as link prediction, node classification, recommendation systems, etc. The aim is to quantitatively study the impact of the random walk parameters(including the number of walks per initial node r, the length of each walk l, the return parameter α, the common neighbor parameter β, and the outgoing parameter γ) on the embedding results. Specifically, the cross entropy is utilized as an observation to compare the difference between the frequency of nodes after random walks and the normalized degree sequence of nodes. The results show that the clustering coefficient significantly impacts the cross entropy. For networks with high clustering coefficient, the value of β should closely approximate that of γ, whereas for networks with low clustering coefficient, the value of β should be significantly smaller than that of γ. The value of α should be less than or equal to the minimum values between β and γ. Finally, the embedding effects of different random walk parameters are tested using node classification and link prediction tasks in real-world networks, and the results indicate that cross entropy can provide guidance for obtaining high-quality node embedding.

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The random walk-based gravity model to identify influential nodes in complex networks

Cited by 49 publications

References 44 publications

Understanding the structure and determinants of economic linkage network: The case of three major city clusters in Yangtze River Economic belt

Understanding the structure and determinants of economic linkage network: The case of three major city clusters in Yangtze River Economic belt

Identifying influential nodes in complex networks based on network embedding and local structure entropy

Quantitative study of random walk parameters in node2vec model

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