A novel method for identifying influential nodes in complex networks based on gravity model

Abstract How to identify influential nodes in complex networks is an essential issue in the study of network characteristics. A number of methods have been proposed to address this problem, but most of them focus on only one aspect. Based on the gravity model, a novel method is proposed for identifying influential nodes in terms of the local topology and the global location. This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes, replaces the shortest distance with a probabilistically motivated effective distance, and fully considers the influence of nodes and their neighbors from the aspect of gravity. On eight real-world networks from different fields, the monotonicity index, susceptible-infected-recovered (SIR) model, and Kendall's tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods. The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.

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Identifying influential nodes in complex networks: Effective distance gravity model

Information Sciences ◽

10.1016/j.ins.2021.01.053 ◽

2021 ◽

Author(s):

Qiuyan Shang ◽

Yong Deng ◽

Kang Hao Cheong

Keyword(s):

Complex Networks ◽

Gravity Model ◽

Effective Distance ◽

Influential Nodes

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A novel method to identify multiple influential nodes in complex networks

Scientia Sinica Informationis ◽

10.1360/n112019-00041 ◽

2019 ◽

Vol 49 (10) ◽

pp. 1333-1342

Author(s):

Xiaoyu LI ◽

Mingyang ZHOU ◽

Xiangyang WU ◽

Binghong WANG ◽

Liao LUO ◽

...

Keyword(s):

Complex Networks ◽

Novel Method ◽

Influential Nodes

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A Novel Method of Identifying Influential Nodes in Complex Networks Based on Random Walks

Journal of Information and Computational Science ◽

10.12733/jics20105091 ◽

2014 ◽

Vol 11 (18) ◽

pp. 6735-6740 ◽

Cited By ~ 2

Author(s):

Tingping Zhang

Keyword(s):

Complex Networks ◽

Random Walks ◽

Novel Method ◽

Influential Nodes

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Improved gravity model for identifying the inﬂuential nodes

EPL (Europhysics Letters) ◽

10.1209/0295-5075/ac49d1 ◽

2022 ◽

Author(s):

Yun Chen ◽

Qiang Guo ◽

Min Liu ◽

Jianguo Liu

Keyword(s):

Dynamic Analysis ◽

Correlation Coefficient ◽

Gravity Model ◽

Betweenness Centrality ◽

Network Dynamic ◽

Degree Centrality ◽

Interaction Frequency ◽

Eigenvector Centrality ◽

Effective Distance ◽

Influential Nodes

Abstract Identifying the influential nodes in network is essential for network dynamic analysis. In this letter, inspired by the gravity model, we present an improved gravity model (EDGM) to identify the influential nodes in network through the effective distance. Firstly, we calculate the degree of nodes. Then we construct the effective distance combined with the interaction frequency between nodes, so as to establish the effective distance gravity model. Comparing with the susceptible-infected model, the results show that the Kendall' s $\tau$ correlation coefficient of EDGM could enhanced by 2.36\% for the gravity model. Compared with other methods, the Kendall' s $\tau$ correlation coefficient of EDGM could enhanced by 11.55%, 17.29%, 7.17% and 10.00% for the degree centrality, betweenness centrality, eigenvector centrality, and PageRank respectively. The results show that the improved gravity model could effectively identify the influential nodes in network.

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A new method to identify influential nodes based on combining of existing centrality measures

Modern Physics Letters B ◽

10.1142/s0217984917502438 ◽

2017 ◽

Vol 31 (26) ◽

pp. 1750243 ◽

Cited By ~ 12

Author(s):

Liguo Fei ◽

Hongming Mo ◽

Yong Deng

Keyword(s):

Complex Networks ◽

Lower Limit ◽

Information Flow ◽

New Method ◽

Centrality Measures ◽

Centrality Measure ◽

Upper Limit ◽

Open Issue ◽

Novel Method ◽

Influential Nodes

How to identify influential nodes in complex networks continues to be an open issue. A number of centrality measures have been presented to address this problem. However, these studies focus only on a centrality measure and each centrality measure has its own shortcomings and limitations. To solve the above problems, in this paper, a novel method is proposed to identify influential nodes based on combining of the existing centrality measures. Because information flow spreads in different ways in different networks, in the specific network, the appropriate centrality measures should be selected to calculate the ranking of nodes. Then, an interval can be generated for the ranking of each node, which includes the upper limit and lower limit obtained from different centrality measures. Next, the final ranking of each node can be determined based on the median of the interval. In order to illustrate the effectiveness of the proposed method, four experiments are conducted to identify vital nodes simulations on four real networks, and the superiority of the method can be demonstrated by the results of comparison experiments.

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An improved gravity model to identify influential nodes in complex networks based on k-shell method

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.107198 ◽

2021 ◽

pp. 107198

Author(s):

Xuan Yang ◽

Fuyuan Xiao

Keyword(s):

Complex Networks ◽

Gravity Model ◽

Influential Nodes

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Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks

Complexity ◽

10.1155/2021/8403738 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Aman Ullah ◽

Bin wang ◽

Jinfang Sheng ◽

Jun Long ◽

Nasrullah Khan

Keyword(s):

Complex Networks ◽

Real World ◽

Practical Significance ◽

Centrality Measures ◽

Sir Epidemic Model ◽

Effective Distance ◽

H Index ◽

Sir Epidemic ◽

Spreading Dynamics ◽

Influential Nodes

Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. A valuable number of approaches have been developed and deployed in these areas where just a few have used centrality measures along with their concerning deficiencies and limitations in their studies. Therefore, to resolve these challenging issues, we propose a novel effective distance-based centrality (EDBC) algorithm for the identification of influential nodes in concerning networks. EDBC algorithm comprises factors such as the power of K-shell, degree nodes, effective distance, and numerous levels of neighbor’s influence or neighborhood potential. The performance of the proposed algorithm is evaluated on nine real-world networks, where a susceptible infected recovered (SIR) epidemic model is employed to examine the spreading dynamics of each node. Simulation results demonstrate that the proposed algorithm outperforms the existing techniques such as eigenvector, betweenness, closeness centralities, hyperlink-induced topic search, H-index, K-shell, page rank, profit leader, and gravity over a valuable margin.

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A novel method to identify influential nodes in complex networks

International Journal of Modern Physics C ◽

10.1142/s0129183120500229 ◽

2019 ◽

Vol 31 (02) ◽

pp. 2050022

Author(s):

Yuanzhi Yang ◽

Lei Yu ◽

Xing Wang ◽

Siyi Chen ◽

You Chen ◽

...

Keyword(s):

Complex Networks ◽

Comprehensive Evaluation ◽

Ideal Solution ◽

Decision Matrix ◽

Single Measure ◽

Order Preference ◽

Si Model ◽

Novel Method ◽

Influential Nodes ◽

Improved Technology

Identifying influential nodes in complex networks continues to be an open and vital issue, which is of great significance to the robustness and vulnerability of networks. In order to accurately identify influential nodes in complex networks and avoid the deviation in the evaluation of node influence by single measure, a novel method based on improved Technology for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to integrate multiple measures and identify influential nodes. Our method takes into account degree centrality (DC), closeness centrality (CC) and betweenness centrality (BC), and uses the information of the decision matrix to objectively assign weight to each measure, and takes the closeness degree from each node to be the ideal solution as the basis for comprehensive evaluation. At last, four experiments based on the Susceptible-Infected (SI) model are carried out, and the superiority of our method can be demonstrated.

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