As a variant of the conventional IM problem, Kim and Yoneki [8] introduced the problem called Influential neighbor selection (INS) to select the most influential neighbors of a node, rather than the most influential arbitrary nodes in a network.
After estimating the neighbors' influences, s selects the top min k,d(s)) nodes with the highest influence values from N s); that is, for the IC model in Section 'Influential neighbor selection problem', these nodes are selected as the set of initially activated nodes S 0⊆V.
We used the IC model described in Section 'Influential neighbor selection problem' to evaluate the performance of the schemes presented in Section 'Neighbor selection schemes', with varying the number of initially activated neighbors k.
For the INS problem described in Section 'Influential neighbor selection problem,' we basically use a greedy strategy to select the influential neighbors.
In Section 'Influential neighbor selection problem', we formally define the INS problem and notations.
Finally, we discuss the effects of the decay factor γ presented in Section 'Influential neighbor selection problem'.
In the pico range, under 5 kW, the requirements are often different to that of larger scale turbines and qualitative requirements become more influential in selection.
The propagation probability λ u,v,s,r) on an edge (u,v ∈E was defined with the spreader s∈V and a piece of information r described in Section 'Influential neighbor selection problem'.
We begin with the definition of the Independent Cascade (IC) model [2] and then introduce the Influential Neighbor Selection (INS) problem, which will be used in the rest of the paper.
Kim and Yoneki [8] recently introduced the problem called Influential Neighbor Selection (INS) where a spreader s spreads a piece of information through carefully chosen k neighbors of hers instead of a set of any arbitrary k nodes in a network.
