The Influence Maximization in Complex Networks: Significant Trends, Leading Contributors, and Prospective Directions

Elaf Adel Abbas, Raaid Alubady, Aqeel Sahi, Mohammed Diykh and Shahab Abdulla

Complexity, 2025, vol. 2025, 1-17

Abstract: Influence maximization (IM) is a concept in social network analysis and data science that focuses on finding the most influential nodes (people, users, etc.) in a network to maximize the spread of information, behavior, or influence. 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, significant contributors, and developing themes. The three primary issues the study attempts to answer are finding the most productive journals, nations, and scholars in IM research; assessing the growth and influence of publications; and predicting future research trends. The results show that IM research is dominated by China and the US, with significant contributions from organizations like the Department of Computer Science and Microsoft Research Asia. The development of the field toward scalable algorithms and practical applications is highlighted by highly cited articles, such as Chen’s (2009) work on successful instant messaging. The investigation also shows the possibility of incorporating AI into future advancements and points out shortcomings in behaviorally informed techniques. This study offers a valuable summary of information management research for academics and professionals trying to understand this ever-evolving topic.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:7605463

DOI: 10.1155/cplx/7605463

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