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Topological to deep learning era for identifying influencers in online social networks :a systematic review

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Abstract

Influential user detection in social media networks involves identifying users who have a significant impact on the network’s dynamics and can shape opinions and behaviours of other users. This paper reviews different topological and deep learning techniques for identifying influencers in online social networks. It examines various methods, such as degree centrality, closeness centrality, betweenness centrality, PageRank, and graph convolutional networks, and compares their strengths and limitations in terms of computational complexity, accuracy, and robustness. The paper aims to provide insights into the state-of-the-art techniques for identifying influencers in online social networks, and to highlight future research directions in this field. The findings of this review paper will be particularly valuable for researchers and practitioners interested in social network analysis.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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    Yasir Rashid & Javaid Iqbal Bhat

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Rashid, Y., Bhat, J.I. Topological to deep learning era for identifying influencers in online social networks :a systematic review. Multimed Tools Appl 83, 14671–14714 (2024). https://doi.org/10.1007/s11042-023-16002-8

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