PPEIM: A preference path-based early-stage influence accumulation model for influential nodes identification in locally dense multi-core networks DOI
Yaofang Zhang, Zibo Wang, Yang Liu

et al.

Journal of Computational Science, Journal Year: 2024, Volume and Issue: unknown, P. 102479 - 102479

Published: Nov. 1, 2024

Language: Английский

A novel semi-local centrality to identify influential nodes in complex networks by integrating multidimensional factors DOI
Kun Zhang, Zaiyi Pu, Chuan Jin

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 145, P. 110177 - 110177

Published: Feb. 8, 2025

Language: Английский

Citations

1
Development of a multidimensional centrality metric for ranking nodes in complex networks DOI
Bo Meng, Amin Rezaeipanah

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 191, P. 115843 - 115843

Published: Dec. 2, 2024

Language: Английский

Citations

7
High-quality community detection in complex networks based on node influence analysis DOI
Zhiyong Wang, Cuiping Zhang, Rebaz Othman Yahya

et al.

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 182, P. 114849 - 114849

Published: April 17, 2024

Language: Английский

Citations

4
IMNE: Maximizing influence through deep learning-based node embedding in social network DOI
Qian Hu, Jiatao Jiang, Hongfeng Xu

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 88, P. 101609 - 101609

Published: May 19, 2024

Language: Английский

Citations

4
Identifying influential nodes in social networks via improved Laplacian centrality DOI Creative Commons
Xiaoyu Zhu,

Rong‐Xia Hao

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 189, P. 115675 - 115675

Published: Oct. 23, 2024

Language: Английский

Citations

4
Directional Node Strength Entropy centrality: Ranking influential nodes in complex networks DOI Creative Commons
Giridhar Maji

Journal of Computational Mathematics and Data Science, Journal Year: 2025, Volume and Issue: unknown, P. 100112 - 100112

Published: Feb. 1, 2025

Language: Английский

Citations

0
Dynamic Analysis of Influencer Impact on Opinion Formation in Social Networks DOI
Omran Berjawi, Danilo Cavaliere, Giuseppe Fenza

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 394 - 408

Published: Jan. 1, 2025

Language: Английский

Citations

0
DCKH‐CNN: A Multimetric Graph‐Based Convolutional Neural Network for Identifying Key Influential Nodes in Earth Surface Data Linked Networks DOI Open Access
Qinjun Qiu, Jiandong Liu,

Mengqi Hao

et al.

Transactions in GIS, Journal Year: 2025, Volume and Issue: 29(2)

Published: Feb. 28, 2025

ABSTRACT Identifying key influential nodes in Earth surface data association networks is crucial for optimizing the use of scientific data. However, challenges such as network size, complexity, and dynamic node influence make this task difficult. While deep learning methods have improved recognition accuracy reduced computational costs complex networks, they still struggle with balancing efficiency accuracy. To address this, we propose DCKH‐CNN, a novel Multimetric Graph‐Based Convolutional Neural Network framework. Based on LCNN model, it integrates global local features by calculating metrics degree centrality, K ‐shell, H ‐index, near‐centrality. One‐hop two‐hop adjacency matrices are used to represent internode relationships, enhancing feature representation. Trained small‐scale model captures unique characteristics. Experimental results using SIR demonstrate that DCKH‐CNN surpasses state‐of‐the‐art algorithms vast majority Surface Data Linked (ESSDLN) datasets real‐world accuracy, while demonstrating moderate time consumption. This method offers more efficient approach identifying supporting accurate recommendations intelligent analysis

Language: Английский

Citations

0
Finding influential nodes in complex networks by integrating nodal intrinsic and extrinsic centrality DOI
Xiaoyu Zhu,

Rong‐Xia Hao

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 194, P. 116278 - 116278

Published: March 12, 2025

Language: Английский

Citations

0
A Landscape-Aware Discrete Particle Swarm Optimization for the Influence Maximization Problem in Social Networks DOI Open Access

Baoqiang Chai,

Junwei Fu, Ruisheng Zhang

et al.

Symmetry, Journal Year: 2025, Volume and Issue: 17(3), P. 435 - 435

Published: March 14, 2025

Influence maximization (IM) is a pivotal challenge in social network analysis, which aims to identify subset of key nodes that can maximize the information spread across networks. Traditional methods often sacrifice solution accuracy for spreading efficiency, while meta-heuristic approaches face limitations escaping local optima and balancing exploration exploitation. To address such challenges, this paper introduces landscape-aware discrete particle swarm optimization (LA-DPSO) solve IM problem. The proposed algorithm employs population partitioning strategy based on fitness distance correlation index enhance diversity. For two partitioned subpopulations, global evolutionary mechanism variable neighborhood search are designed make symmetrical balance between landscape entropy introduced detect prevent from premature convergence during evolution. Experiments conducted six real-world networks demonstrate LA-DPSO achieves an average performance improvement 16% compared state-of-the-art exhibiting excellent scalability diverse types.

Language: Английский

Citations

0