Hybrid Global Structure Model for Unraveling Influential Nodes in Complex Networks

被引:0
作者
Mukhtar, Mohd Fariduddin [1 ,2 ]
Abas, Zuraida Abal [1 ]
Rasib, Amir Hamzah Abdul [2 ]
Anuar, Siti Haryanti Hairol [1 ]
Zaki, Nurul Hafizah Mohd [1 ]
Rahman, Ahmad Fadzli Nizam Abdul [1 ]
Abidin, Zaheera Zainal [1 ]
Shibghatullah, Abdul Samad [3 ]
机构
[1] Univ Teknikal Malaysia Melaka, Fak Teknol Maklumat & Komunikasi, Melaka, Malaysia
[2] Univ Teknikal Malaysia Melaka, Fak Teknol Kejuruteraan Mekanikal & Pembuatan, Melaka, Malaysia
[3] UCSI Univ, Inst Comp Sci & Digital Innovat, Kuala Lumpur 56000, Malaysia
关键词
Centrality indices; combination; hybrid; global structure model; influential nodes; CENTRALITY; SPREADERS;
D O I
10.14569/IJACSA.2023.0140677
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In graph analytics, the identification of influential nodes in real-world networks plays a crucial role in understanding network dynamics and enabling various applications. However, traditional centrality metrics often fall short in capturing the interplay between local and global network information. To address this limitation, the Global Structure Model (GSM) and its improved version (IGSM) have been proposed. Nonetheless, these models still lack an adequate representation of path length. This research aims to enhance existing approaches by developing a hybrid model called H-GSM. The H-GSM algorithm integrates the GSM framework with local and global centrality measurements, specifically Degree Centrality (DC) and K-Shell Centrality (KS). By incorporating these additional measures, the H-GSM model strives to improve the accuracy of identifying influential nodes in complex networks. To evaluate the effectiveness of the H-GSM model, real-world datasets are employed, and comparative analyses are conducted against existing techniques. The results demonstrate that the H-GSM model outperforms these techniques, showcasing its enhanced performance in identifying influential nodes. As future research directions, it is proposed to explore different combinations of index styles and centrality measures within the H-GSM framework.
引用
收藏
页码:724 / 730
页数:7
相关论文
共 50 条
[41]   A novel method to identify influential nodes in complex networks [J].
Yang, Yuanzhi ;
Yu, Lei ;
Wang, Xing ;
Chen, Siyi ;
Chen, You ;
Zhou, Yipeng .
INTERNATIONAL JOURNAL OF MODERN PHYSICS C, 2020, 31 (02)
[42]   Identifying influential nodes in complex networks based on AHP [J].
Bian, Tian ;
Hu, Jiantao ;
Deng, Yong .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2017, 479 :422-436
[43]   Identification of nodes influence based on global structure model in complex networks [J].
Ullah, Aman ;
Wang, Bin ;
Sheng, JinFang ;
Long, Jun ;
Khan, Nasrullah ;
Sun, ZeJun .
SCIENTIFIC REPORTS, 2021, 11 (01)
[44]   An improved gravity model to identify influential nodes in complex networks based on k-shell method [J].
Yang, Xuan ;
Xiao, Fuyuan .
KNOWLEDGE-BASED SYSTEMS, 2021, 227 (227)
[45]   The random walk-based gravity model to identify influential nodes in complex networks [J].
Zhao, Jie ;
Wen, Tao ;
Jahanshahi, Hadi ;
Cheong, Kang Hao .
INFORMATION SCIENCES, 2022, 609 :1706-1720
[46]   A novel method for identifying influential nodes in complex networks based on multiple attributes [J].
Liu, Dong ;
Nie, Hao ;
Zhang, Baowen .
INTERNATIONAL JOURNAL OF MODERN PHYSICS B, 2018, 32 (28)
[47]   Finding Influential Nodes in Complex Networks Using Nearest Neighborhood Trust Value [J].
Hajarathaiah, Koduru ;
Enduri, Murali Krishna ;
Anamalamudi, Satish .
COMPLEX NETWORKS & THEIR APPLICATIONS X, VOL 2, 2022, 1016 :253-264
[48]   A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy [J].
Chen, Xuegong ;
Zhou, Jie ;
Liao, Zhifang ;
Liu, Shengzong ;
Zhang, Yan .
ENTROPY, 2020, 22 (08)
[49]   Identifying influential nodes in complex networks based on the inverse-square law [J].
Fei, Liguo ;
Zhang, Qi ;
Deng, Yong .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 512 :1044-1059
[50]   Isolating Coefficient-Based Framework to Recognize Influential Nodes in Complex Networks [J].
Mohammad, Buran Basha ;
Dhuli, V. Sateeshkrishna ;
Enduri, Murali Krishna ;
Cenkeramaddi, Linga Reddy .
IEEE ACCESS, 2024, 12 :183875-183900