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• W4285204071 abstract "In social network analysis, the influence maximization problem recognizes a small set of seed nodes that effectively maximizes the aggregated influence under a cascading propagation model. The approach has vast implications in viral marketing, government decision making, epidemic control, and many more. In the last decades, researchers developed many strategies that effectively identify the seed nodes. Although, the seed identification process is an NP-hard problem. Sometimes, due to the network structure, the best-known seeds are incapable of propagating influence throughout the network. We observed that a tiny structural modification by adding a few links sometimes increases the aggregated influence notably. From the literature, we have observed that no prior work exists in this regard. In this paper, first, we have applied multi-objective optimization for identifying initial seeds considering aggregated influence and timestep to achieve the coverage as objectives. Then for suitable non-dominated seeds, the proposed approach computed the minimum number of required missing links against every uninfluenced node to sustain the propagation. Finally, a set of statistical techniques and locally globally tuned biogeography-based optimization are used to identify end vertices for the links recommendation. The recommended links connect non-influenced components to the influenced component and allow further influence propagation." @default.
• W4285204071 created "2022-07-14" @default.
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• W4285204071 date "2022-01-01" @default.
• W4285204071 modified "2023-09-27" @default.
• W4285204071 title "Link Recommendation for Social Influence Maximization" @default.
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• W4285204071 doi "https://doi.org/10.1007/978-981-16-8739-6_7" @default.
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