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• W2963149780 abstract "Most prior algorithms for influence maximization focused are designed for Online Social Networks (OSNs) and require centralized computation. Directly deploying the above algorithms in distributed Mobile Social Networks (MSNs) will overwhelm the networks due to an enormous number of messages required for seed selection. In this paper, therefore, we design a new cross-layer strategy to jointly examine MSN and mobile ad hoc networks (MANETs) to facilitate efficient seed selection, by extracting a subset of nodes as agents to represent nearby friends during the distributed computation. Specifically, we formulate a new optimization problem, named Agent Selection Problem (ASP), to minimize the message overhead transmitted in MANET. We prove that ASP is NP-Hard and design an effectively distributed algorithm. Simulation results in real and synthetic datasets manifest that the message overhead can be significantly reduced compared with the existing approaches." @default.
• W2963149780 created "2019-07-30" @default.
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• W2963149780 date "2016-12-01" @default.
• W2963149780 modified "2023-09-24" @default.
• W2963149780 title "Cross-Layer Design of Influence Maximization in Mobile Social Networks" @default.
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• W2963149780 doi "https://doi.org/10.1109/glocom.2016.7841707" @default.
• W2963149780 hasPublicationYear "2016" @default.
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