A k-hop Collaborate Game Model: Adaptive Strategy to Maximize Total Revenue
Jianxiong Guo, Weili Wu

TL;DR
This paper introduces a k-hop Collaborate Game Model in OSNs to maximize revenue through adaptive strategies, addressing the NP-hardness of the problem and proposing efficient algorithms validated by simulations.
Contribution
It formulates the Revenue Maximization under k-hop Collaborate Game problem, proves its complexity, and develops practical algorithms with theoretical guarantees.
Findings
The problem is NP-hard but admits a $(1-1/e)$-approximation in special cases.
Proposed algorithms are efficient and effective in real-world social network simulations.
The model accurately captures influence within k-hop neighborhoods for revenue maximization.
Abstract
In Online Social Networks (OSNs), interpersonal communication and information sharing are happening all the time, and it is real-time. When a user initiates an activity in OSNs, immediately, he/she will have a certain influence in his/her friendship circle naturally, some users in the initiator's friendship circle will be attracted to participate in this activity. Based on such a fact, we design a k-hop Collaborate Game Model, which means that an activity initiated by a user can only influence those users whose distance are within k-hop from this initiator in OSNs. Besides, we introduce the problem of Revenue Maximization under k-hop Collaborate Game (RMKCG), which identifies a limited number of initiators in order to obtain revenue as much as possible. Collaborate Game Model describes in detail how to quantify revenue and the logic behind it. We do not know how many followers would be…
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