Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures
The study addresses the limitations of traditional centrality measures in complex networks, especially in disease-spreading situations, due to their inability to fully grasp the intricate connection between a node’s functional importance and structural attributes. To tackle this issue, th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10401904/ |
| _version_ | 1797348703060623360 |
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| author | Koduru Hajarathaiah Murali Krishna Enduri Satish Anamalamudi Ashu Abdul Jenhui Chen |
| author_facet | Koduru Hajarathaiah Murali Krishna Enduri Satish Anamalamudi Ashu Abdul Jenhui Chen |
| author_sort | Koduru Hajarathaiah |
| collection | DOAJ |
| description | The study addresses the limitations of traditional centrality measures in complex networks, especially in disease-spreading situations, due to their inability to fully grasp the intricate connection between a node’s functional importance and structural attributes. To tackle this issue, the research introduces an innovative framework that employs machine learning techniques to evaluate the significance of nodes in transmission scenarios. This framework incorporates various centrality measures like degree, clustering coefficient, Katz, local relative change in average clustering coefficient, average Katz, and average degree (LRACC, LRAK, and LRAD) to create a feature vector for each node. These methods capture diverse topological structures of nodes and incorporate the infection rate, a critical factor in understanding propagation scenarios. To establish accurate labels for node significance, propagation tests are simulated using epidemic models (SIR and Independent Cascade models). Machine learning methods are employed to capture the complex relationship between a node’s true spreadability and infection rate. The performance of the machine learning model is compared to traditional centrality methods in two scenarios. In the first scenario, training and testing data are sourced from the same network, highlighting the superior accuracy of the machine learning approach. In the second scenario, training data from one network and testing data from another are used, where LRACC, LRAK, and LRAD outperform the machine learning methods. |
| first_indexed | 2024-03-08T12:09:47Z |
| format | Article |
| id | doaj.art-8b944a33614a44b094363d811f1f5099 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T12:09:47Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8b944a33614a44b094363d811f1f50992024-01-23T00:03:29ZengIEEEIEEE Access2169-35362024-01-0112101861020110.1109/ACCESS.2024.335509610401904Node Significance Analysis in Complex Networks Using Machine Learning and Centrality MeasuresKoduru Hajarathaiah0https://orcid.org/0000-0001-9504-2307Murali Krishna Enduri1https://orcid.org/0000-0002-9029-2187Satish Anamalamudi2Ashu Abdul3https://orcid.org/0000-0003-0221-8225Jenhui Chen4https://orcid.org/0000-0002-8372-9221School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, Algorithms and Complexity Theory Laboratory, SRM University-AP, Amaravati, IndiaDepartment of Computer Science and Engineering, Algorithms and Complexity Theory Laboratory, SRM University-AP, Amaravati, IndiaDepartment of Computer Science and Engineering, Centre for Computational and Integrative Sciences, SRM University-AP, Amaravati, IndiaDepartment of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan, TaiwanThe study addresses the limitations of traditional centrality measures in complex networks, especially in disease-spreading situations, due to their inability to fully grasp the intricate connection between a node’s functional importance and structural attributes. To tackle this issue, the research introduces an innovative framework that employs machine learning techniques to evaluate the significance of nodes in transmission scenarios. This framework incorporates various centrality measures like degree, clustering coefficient, Katz, local relative change in average clustering coefficient, average Katz, and average degree (LRACC, LRAK, and LRAD) to create a feature vector for each node. These methods capture diverse topological structures of nodes and incorporate the infection rate, a critical factor in understanding propagation scenarios. To establish accurate labels for node significance, propagation tests are simulated using epidemic models (SIR and Independent Cascade models). Machine learning methods are employed to capture the complex relationship between a node’s true spreadability and infection rate. The performance of the machine learning model is compared to traditional centrality methods in two scenarios. In the first scenario, training and testing data are sourced from the same network, highlighting the superior accuracy of the machine learning approach. In the second scenario, training data from one network and testing data from another are used, where LRACC, LRAK, and LRAD outperform the machine learning methods.https://ieeexplore.ieee.org/document/10401904/Complex networksinfluential nodeslocal centralitiesmachine learning techniques |
| spellingShingle | Koduru Hajarathaiah Murali Krishna Enduri Satish Anamalamudi Ashu Abdul Jenhui Chen Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures IEEE Access Complex networks influential nodes local centralities machine learning techniques |
| title | Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures |
| title_full | Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures |
| title_fullStr | Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures |
| title_full_unstemmed | Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures |
| title_short | Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures |
| title_sort | node significance analysis in complex networks using machine learning and centrality measures |
| topic | Complex networks influential nodes local centralities machine learning techniques |
| url | https://ieeexplore.ieee.org/document/10401904/ |
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