Entropy-based weighted multi-channel convolutional neural network method for node importance assessment DOI Open Access

JIANG Tingshuai,

Yirun Ruan, Hai Li

и другие.

Acta Physica Sinica, Год журнала: 2025, Номер 74(12), С. 0 - 0

Опубликована: Янв. 1, 2025

Identifying key nodes in complex networks or evaluating the relative node importance with respect to others using quantitative methods is a fundamental issue network science. To address limitations of existing approaches—namely subjectivity assigning weights indicators and insufficient integration global local structural information—this paper proposes an entropy-weighted multi-channel convolutional neural framework (EMCNN). First, parameter-free entropy-based weight allocation model constructed dynamically assign multiple by computing their entropy values, thereby mitigating inherent traditional parameter-setting enhancing objectivity indicator fusion. Second, features are decoupled reconstructed into separate channels form feature maps, which significantly enhance representational capacity structure. Third, leveraging extraction capabilities power attention mechanisms, extracts deep representations from while emphasizing information through attention-based weighting, thus enabling more accurate identification characterization importance. validate effectiveness proposed method, extensive experiments conducted on nine real-world SIR spreading model, assessing performance terms correlation, accuracy, robustness. The Kendall correlation coefficient employed as primary evaluation metric measure consistency between predicted actual influence. Additionally, performed three representative synthetic further test model’s generalizability. Experimental results demonstrate that EMCNN consistently effectively evaluates influence under varying transmission rates, outperforms mainstream algorithms both accuracy. These findings highlight method’s strong generalization ability broad applicability tasks within networks.

Язык: Английский

Finding influential nodes via graph embedding and hybrid centrality in complex networks DOI
Aman Ullah, Muhammad Ali Naeem

Chaos Solitons & Fractals, Год журнала: 2025, Номер 194, С. 116151 - 116151

Опубликована: Фев. 25, 2025

Язык: Английский

Процитировано

4

Integrating local and global information to identify influential nodes in complex networks DOI Creative Commons
Mohd Fariduddin Mukhtar, Zuraida Abal Abas,

Azhari Samsu Baharuddin

и другие.

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Июль 14, 2023

Abstract Centrality analysis is a crucial tool for understanding the role of nodes in network, but it unclear how different centrality measures provide much unique information. To improve identification influential we propose new method called Hybrid-GSM (H-GSM) that combines K-shell decomposition approach and Degree Centrality. H-GSM characterizes impact more precisely than Global Structure Model (GSM), which cannot distinguish importance each node. We evaluate performance using SIR model to simulate propagation process six real-world networks. Our outperforms other approaches regarding computational complexity, node discrimination, accuracy. findings demonstrate proposed as an effective identifying complex

Язык: Английский

Процитировано

24

A Study on Linguistic Z-Graph and Its Application in Social Networks DOI Creative Commons
Rupkumar Mahapatra, Sovan Samanta, Madhumangal Pal

и другие.

Mathematics, Год журнала: 2024, Номер 12(18), С. 2898 - 2898

Опубликована: Сен. 17, 2024

This paper presents a comprehensive study of the linguistic Z-graph, which is novel framework designed to analyze structures within social networks. By integrating concepts from graph theory and linguistics, Z-graph provides detailed understanding language dynamics in online communities. highlights practical applications Z-graphs identifying central nodes networks, are crucial for businesses market capture information dissemination. Traditional methods rely on direct connections, but network connections often exhibit uncertainty. focuses using fuzzy theory, particularly Z-graphs, address this uncertainty, offering more insights compared graphs. Our introduces new centrality measure enhancing our structures.

Язык: Английский

Процитировано

18

Locating influential nodes in hypergraphs via fuzzy collective influence DOI

S. F. Zhang,

Xiaoyan Yu, Gui‐Quan Sun

и другие.

Communications in Nonlinear Science and Numerical Simulation, Год журнала: 2025, Номер unknown, С. 108574 - 108574

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1

Towards identifying influential nodes in complex networks using semi-local centrality metrics DOI Creative Commons
Kun Zhang,

Yu Zhou,

Haixia Long

и другие.

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2023, Номер 35(10), С. 101798 - 101798

Опубликована: Окт. 16, 2023

The influence of the node refers to ability disseminate information. faster and wider spreads, greater its influence. There are many classical topological metrics that can be used evaluate influencing nodes. Degree centrality, betweenness closeness centrality local among most common for identifying influential nodes in complex networks. is very simple but not effective. Global such as better identify nodes, they compatible on large-scale networks due their high complexity. In order design a ranking method this paper new semi-local metric proposed based relative change average shortest path entire network. Meanwhile, our provides quantitative global importance model measure overall each node. To performance metric, we use Susceptible-Infected-Recovered (SIR) epidemic model. Experimental results several real-world show has competitive with existing equivalent efficiency dealing effectiveness been proven numerical examples Kendall's coefficient.

Язык: Английский

Процитировано

21

Towards investigating influencers in complex social networks using electric potential concept from a centrality perspective DOI
Aman Ullah, Salah Ud Din, Nasrullah Khan

и другие.

Information Fusion, Год журнала: 2024, Номер 109, С. 102439 - 102439

Опубликована: Апрель 26, 2024

Язык: Английский

Процитировано

9

Node importance evaluation method of complex network based on the fusion gravity model DOI

Haoming Guo,

Shuangling Wang,

Xuefeng Yan

и другие.

Chaos Solitons & Fractals, Год журнала: 2024, Номер 183, С. 114924 - 114924

Опубликована: Май 6, 2024

Язык: Английский

Процитировано

8

Vital node identification in complex networks based on autoencoder and graph neural network DOI
You Xiong, Zheng Hu, Chang Su

и другие.

Applied Soft Computing, Год журнала: 2024, Номер 163, С. 111895 - 111895

Опубликована: Июнь 22, 2024

Язык: Английский

Процитировано

8

On the role of diffusion dynamics on community-aware centrality measures DOI Creative Commons
Stephany Rajeh, Hocine Cherifi

PLoS ONE, Год журнала: 2024, Номер 19(7), С. e0306561 - e0306561

Опубликована: Июль 18, 2024

Theoretical and empirical studies on diffusion models have revealed their versatile applicability across different fields, spanning from sociology finance to biology ecology. The presence of a community structure within real-world networks has substantial impact how processes unfold. Key nodes located both between these communities play crucial role in initiating diffusion, community-aware centrality measures effectively identify nodes. While numerous been proposed literature, very few investigate the relationship diffusive ability key selected by measures, distinct dynamical conditions various models, diverse network topologies. By conducting comparative evaluation four utilizing synthetic networks, along with employing two detection techniques, our study aims gain deeper insights into effectiveness measures. Results suggest that power is affected three main factors: strength network’s structure, internal dynamics each model, budget availability. Specifically, category simple contagion such as SI, SIR, IC, we observe similar patterns when remain constant. In contrast, LT which falls under complex dynamics, exhibits divergent behavior same conditions.

Язык: Английский

Процитировано

7

Development of a multidimensional centrality metric for ranking nodes in complex networks DOI
Bo Meng, Amin Rezaeipanah

Chaos Solitons & Fractals, Год журнала: 2024, Номер 191, С. 115843 - 115843

Опубликована: Дек. 2, 2024

Язык: Английский

Процитировано

7