Abstract
Groups play a crucial role in decision-making of social networks, since individual decision-making is often influenced by groups. This brings the Group Influence Maximization (GIM) problem which aims to maximize the expected number of activated groups by finding k seed nodes. The GIM problem has been proved NP-hard while computing the objective function is \(\#P\)-hard under Independent Cascade (IC) model. We propose an algorithm called Maximizing Group Coverage (MGC) which greedily selects the best node based on evaluating the contribution of nodes to the groups, ensuring the success of approximating the maximization of the number of activated groups. Finally, we compare the MGC algorithm with the baseline algorithm called Maximum Coverage (MC) through experiments, demonstrating that MGC outperforms MC under IC model regarding the average number of activated groups.
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Acknowledgements
The first and the second author are supported by Natural Science Foundation of China (No. 61772005). The third author is supported by Natural Science Foundation of Fujian Province (No. 2020J01845) and Educational Research Project for Young and Middle-aged Teachers of Fujian Provincial Department of Education (No. JAT190613).
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Zhong, Y., Guo, L., Huang, P. (2021). Maximizing Group Coverage in Social Networks. In: Zhang, Y., Xu, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2020. Lecture Notes in Computer Science(), vol 12606. Springer, Cham. https://doi.org/10.1007/978-3-030-69244-5_24
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