Abstract
Recently, the demand and utility of online social networks are well accepted to share information and connect people from diverse areas. Online social networks have provided a common platform for frequent human interactions, resulting in a significant increase in information about the individual users, their interactions, and relationships. These users can be classified into different classes based on the similarity and differences in users’ characteristics and their local and global position in the network. The node classification problem has been recognized due to its real-time applications in recommendation systems, epidemiological diffusion, sociological dynamics of communities, and anomaly detection. Diverse attempts have been made to perform informative node classifications. Furthermore, the deep learning based approaches for node classification in online social networks have provided state-of-the-art results with better insights and high accuracy. In this chapter, we provide a rigorous literature review of deep learning based methods designed for node classification, and conclude the chapter with interesting and futuristic open research directions to fill the gap in the current works and the demand of next-generation online social systems.
Keywords
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This work is supported by The Department of Science and Technology, Government of India, sponsored project having Grant no. ‘DST-1401-CSE’.
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Arya, A., Pandey, P.K., Saxena, A. (2022). Node Classification Using Deep Learning in Social Networks. In: Hong, TP., Serrano-Estrada, L., Saxena, A., Biswas, A. (eds) Deep Learning for Social Media Data Analytics. Studies in Big Data, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-10869-3_1
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