Abstract
The online social networks (OSNs) have become the significant elements of information society used for maintaining social relationships. Community structure is the fundamental element of any OSN which is constituted considering the users’ common interests. Coalition formation is the key form of interaction between the users in OSNs for information diffusion. The role of the Shapley value in OSNs is to measure the significance of key/influential users in each community. In this paper, a novel approach is proposed to identify such influential users by considering various node centrality measures using the Shapley value. The bridge nodes among these influential nodes are identified, which will be used for faster disseminating of information in the OSNs. Further, a recommender system to identify the influential nodes is developed based on centrality measure, information flow coefficient, and coordination coefficient to assess more accurately the information flow without any noise. The approach is validated with the popular OSN structures like the ‘Twitter followers’ network and Zachary’s karate club network with the help of the popular statistical programming language ‘R’. From the results obtained, it is observed that the context of the formulation of OSN communities based on their characteristics will provide a better solution.
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Sailaja Kumar, K., Evangelin Geetha, D. (2022). A Recommender System for Information Diffusion. In: Pandian, A.P., Palanisamy, R., Narayanan, M., Senjyu, T. (eds) Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-7330-6_51
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DOI: https://doi.org/10.1007/978-981-16-7330-6_51
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