The Influence Maximization in Complex Networks: Significant Trends, Leading Contributors, and Prospective Directions
Article
| Article Title | The Influence Maximization in Complex Networks: Significant Trends, Leading Contributors, and Prospective Directions |
|---|---|
| ERA Journal ID | 493 |
| Article Category | Article |
| Authors | Abbas, Elaf Adel, Alubady, Raaid, Sahi, Aqeel, Diykh, Mohammed and Abdulla, Shahab |
| Journal Title | Complexity |
| Journal Citation | 2025 (1) |
| Article Number | 7605463 |
| Number of Pages | 17 |
| Year | 2025 |
| Publisher | Hindawi Publishing Corporation |
| Place of Publication | United States |
| ISSN | 1076-2787 |
| 1099-0526 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1155/cplx/7605463 |
| Web Address (URL) | https://onlinelibrary.wiley.com/doi/pdf/10.1155/cplx/7605463 |
| Abstract | Infuence maximization (IM) is a concept in social network analysis and data science that focuses on fnding the most infuential nodes (people, users, etc.) in a network to maximize the spread of information, behavior, or infuence. IM studies have become more crucial due to the quick uptake of social media and networking technologies, which have revolutionized communication and information sharing. Using information from the Scopus database, this study conducts a thorough bibliometric analysis of the literature on instant messaging from 2006 to 2024 to investigate publishing trends, signifcant contributors, and developing themes. Te three primary issues the study attempts to answer are fnding the most productive journals, nations, and scholars in IM research; assessing the growth and infuence of publications; and predicting future research trends. Te results show that IM research is dominated by China and the US, with signifcant contributions from organizations like the Department of Computer Science and Microsoft Research Asia. Te development of the feld toward scalable algorithms and practical applications is highlighted by highly cited articles, such as Chen’s (2009) work on successful instant messaging. Te investigation also shows the possibility of incorporating AI into future advancements and points out shortcomings in behaviorally informed techniques. This study ofers a valuable summary of information management research for academics and professionals trying to understand this ever-evolving topic. |
| Keywords | infuence maximization (IM); bibliometric analysis; complex networks; social network analysis; research trends |
| Article Publishing Charge (APC) Funding | School/Centre |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
| 460810. Social robotics | |
| Byline Affiliations | University of Karbala, Iraq |
| Al-Ayen University, Iraq | |
| School of Science, Engineering & Digital Technologies- Maths,Physics & Computing | |
| UniSQ College |
https://research.usq.edu.au/item/1014x7/the-influence-maximization-in-complex-networks-significant-trends-leading-contributors-and-prospective-directions
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| Complexity - 2025 - Abbas - The Influence Maximization in Complex Networks Significant Trends Leading Contributors and.pdf | ||
| License: CC BY 4.0 | ||
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