Abstract
The Online Social Networks (OSNs) are growing rapidly due to its mass popularity and easy accessibility. Nowadays, OSNs have a major impact on business, healthcare, agriculture, and society. Reaching the target audience efficiently through OSNs is most desirable to various organizations. Identifying influential nodes through online social network analysis enables to reach the target audience most effectively and thus has drawn significant attention from the researchers. In the recent past, several techniques have been proposed for finding influential nodes in OSNs. Though the activity behaviors of individual user play an important role in the influence maximization, most of the works in this area does not consider this. Our current work proposes a model to identify top-k influential nodes by evaluating Normalized Influence Factors of network members based on their activity behaviors in egocentric OSNs.
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Debnath, S., Sarkar, D., Jana, P. (2020). Top-k Influential Nodes Identification Based on Activity Behaviors in Egocentric Online Social Networks. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_39
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DOI: https://doi.org/10.1007/978-981-13-9042-5_39
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