  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.
