ICDC: Ranking Influential Nodes in Complex Networks Based on Isolating and Clustering Coefficient Centrality Measures
Over the past decade, there has been extensive research conducted on complex networks, primarily driven by their crucial role in understanding the various real-world networks such as social networks, communication networks, transportation networks, and biological networks. Ranking influential nodes...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10299618/ |
| _version_ | 1797627792767057920 |
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| author | Mondikathi Chiranjeevi V Sateeshkrishna Dhuli Murali Krishna Enduri Linga Reddy Cenkeramaddi |
| author_facet | Mondikathi Chiranjeevi V Sateeshkrishna Dhuli Murali Krishna Enduri Linga Reddy Cenkeramaddi |
| author_sort | Mondikathi Chiranjeevi |
| collection | DOAJ |
| description | Over the past decade, there has been extensive research conducted on complex networks, primarily driven by their crucial role in understanding the various real-world networks such as social networks, communication networks, transportation networks, and biological networks. Ranking influential nodes is one of the fundamental research problems in the areas of rumor spreading, disease research, viral marketing, and drug development. Influential nodes in any network are used to disseminate the information as fast as possible. Centrality measures are designed to quantify the node’s significance and rank the influential nodes in complex networks. However, these measures typically focus on either the local or global topological structure within and outside network communities. In particular, many measures limit their ability to capture the node’s overall impact on small-scale networks. To address these challenges, we develop a novel centrality measure called Isolating Clustering Distance Centrality (ICDC) by integrating the isolating and clustering coefficient centrality measures. The proposed metric gives a more thorough assessment of the node’s importance by integrating the local isolation and global topological influence in large-scale complex networks. We employ the SIR and ICM epidemic models to study the efficiency of ICDC against traditional centrality measures across real-world complex networks. Our experimental findings consistently highlight the superior efficacy of ICDC in terms of fast spreading and computational efficiency when compared to existing centrality measures. |
| first_indexed | 2024-03-11T10:30:30Z |
| format | Article |
| id | doaj.art-405c3bce286f483681315e9fc734bf5f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T10:30:30Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-405c3bce286f483681315e9fc734bf5f2023-11-15T00:00:45ZengIEEEIEEE Access2169-35362023-01-011112619512620810.1109/ACCESS.2023.332834510299618ICDC: Ranking Influential Nodes in Complex Networks Based on Isolating and Clustering Coefficient Centrality MeasuresMondikathi Chiranjeevi0https://orcid.org/0009-0008-1490-9406V Sateeshkrishna Dhuli1Murali Krishna Enduri2https://orcid.org/0000-0002-9029-2187Linga Reddy Cenkeramaddi3https://orcid.org/0000-0002-1023-2118Department of Electronics and Communications Engineering, SRM University-AP, Amaravati, IndiaDepartment of Electronics and Communications Engineering, SRM University-AP, Amaravati, IndiaDepartment of Computer Science and Engineering, Algorithms and Complexity Theory Laboratory, SRM University-AP, Amaravati, IndiaDepartment of Information and Communication Technology, University of Agder (UiA), Grimstad, NorwayOver the past decade, there has been extensive research conducted on complex networks, primarily driven by their crucial role in understanding the various real-world networks such as social networks, communication networks, transportation networks, and biological networks. Ranking influential nodes is one of the fundamental research problems in the areas of rumor spreading, disease research, viral marketing, and drug development. Influential nodes in any network are used to disseminate the information as fast as possible. Centrality measures are designed to quantify the node’s significance and rank the influential nodes in complex networks. However, these measures typically focus on either the local or global topological structure within and outside network communities. In particular, many measures limit their ability to capture the node’s overall impact on small-scale networks. To address these challenges, we develop a novel centrality measure called Isolating Clustering Distance Centrality (ICDC) by integrating the isolating and clustering coefficient centrality measures. The proposed metric gives a more thorough assessment of the node’s importance by integrating the local isolation and global topological influence in large-scale complex networks. We employ the SIR and ICM epidemic models to study the efficiency of ICDC against traditional centrality measures across real-world complex networks. Our experimental findings consistently highlight the superior efficacy of ICDC in terms of fast spreading and computational efficiency when compared to existing centrality measures.https://ieeexplore.ieee.org/document/10299618/Influential nodesisolating centralityclustering coefficientisolating clustering distance centrality |
| spellingShingle | Mondikathi Chiranjeevi V Sateeshkrishna Dhuli Murali Krishna Enduri Linga Reddy Cenkeramaddi ICDC: Ranking Influential Nodes in Complex Networks Based on Isolating and Clustering Coefficient Centrality Measures IEEE Access Influential nodes isolating centrality clustering coefficient isolating clustering distance centrality |
| title | ICDC: Ranking Influential Nodes in Complex Networks Based on Isolating and Clustering Coefficient Centrality Measures |
| title_full | ICDC: Ranking Influential Nodes in Complex Networks Based on Isolating and Clustering Coefficient Centrality Measures |
| title_fullStr | ICDC: Ranking Influential Nodes in Complex Networks Based on Isolating and Clustering Coefficient Centrality Measures |
| title_full_unstemmed | ICDC: Ranking Influential Nodes in Complex Networks Based on Isolating and Clustering Coefficient Centrality Measures |
| title_short | ICDC: Ranking Influential Nodes in Complex Networks Based on Isolating and Clustering Coefficient Centrality Measures |
| title_sort | icdc ranking influential nodes in complex networks based on isolating and clustering coefficient centrality measures |
| topic | Influential nodes isolating centrality clustering coefficient isolating clustering distance centrality |
| url | https://ieeexplore.ieee.org/document/10299618/ |
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