A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics
Online social networks are increasingly connecting people around the world. Influence maximization is a key area of research in online social networks, which identifies influential users during information dissemination. Most of the existing influence maximization methods only consider the transmiss...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2021.9020024 |
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| author | Jie Tong Leilei Shi Lu Liu John Panneerselvam Zixuan Han |
| author_facet | Jie Tong Leilei Shi Lu Liu John Panneerselvam Zixuan Han |
| author_sort | Jie Tong |
| collection | DOAJ |
| description | Online social networks are increasingly connecting people around the world. Influence maximization is a key area of research in online social networks, which identifies influential users during information dissemination. Most of the existing influence maximization methods only consider the transmission of a single channel, but real-world networks mostly include multiple channels of information transmission with competitive relationships. The problem of influence maximization in an environment involves selecting the seed node set for certain competitive information, so that it can avoid the influence of other information, and ultimately affect the largest set of nodes in the network. In this paper, the influence calculation of nodes is achieved according to the local community discovery algorithm, which is based on community dispersion and the characteristics of dynamic community structure. Furthermore, considering two various competitive information dissemination cases as an example, a solution is designed for self-interested information based on the assumption that the seed node set of competitive information is known, and a novel influence maximization algorithm of node avoidance based on user interest is proposed. Experiments conducted based on real-world Twitter dataset demonstrates the efficiency of our proposed algorithm in terms of accuracy and time against notable influence maximization algorithms. |
| format | Article |
| id | doaj-art-0695152629124cbb946916488f40f016 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-0695152629124cbb946916488f40f0162025-02-02T06:14:03ZengTsinghua University PressBig Data Mining and Analytics2096-06542022-06-015213013910.26599/BDMA.2021.9020024A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data AnalyticsJie Tong0Leilei Shi1Lu Liu2John Panneerselvam3Zixuan Han4School of Computer Science and Telecommunication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Telecommunication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UKSchool of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UKSchool of Computer Science and Telecommunication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, ChinaOnline social networks are increasingly connecting people around the world. Influence maximization is a key area of research in online social networks, which identifies influential users during information dissemination. Most of the existing influence maximization methods only consider the transmission of a single channel, but real-world networks mostly include multiple channels of information transmission with competitive relationships. The problem of influence maximization in an environment involves selecting the seed node set for certain competitive information, so that it can avoid the influence of other information, and ultimately affect the largest set of nodes in the network. In this paper, the influence calculation of nodes is achieved according to the local community discovery algorithm, which is based on community dispersion and the characteristics of dynamic community structure. Furthermore, considering two various competitive information dissemination cases as an example, a solution is designed for self-interested information based on the assumption that the seed node set of competitive information is known, and a novel influence maximization algorithm of node avoidance based on user interest is proposed. Experiments conducted based on real-world Twitter dataset demonstrates the efficiency of our proposed algorithm in terms of accuracy and time against notable influence maximization algorithms.https://www.sciopen.com/article/10.26599/BDMA.2021.9020024influence maximizationcompetitive environmentdynamic network |
| spellingShingle | Jie Tong Leilei Shi Lu Liu John Panneerselvam Zixuan Han A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics Big Data Mining and Analytics influence maximization competitive environment dynamic network |
| title | A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics |
| title_full | A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics |
| title_fullStr | A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics |
| title_full_unstemmed | A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics |
| title_short | A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics |
| title_sort | novel influence maximization algorithm for a competitive environment based on social media data analytics |
| topic | influence maximization competitive environment dynamic network |
| url | https://www.sciopen.com/article/10.26599/BDMA.2021.9020024 |
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