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Dynamic Path Relinking for the Target Set Selection problem
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DOI：10.1016/j.knosys.2023.110827.png)
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Abstract
En 中文
This research proposes the use of metaheuristics for solving the Target Set Selection (TSS) problem. This problem emerges in the context of influence maximization problems, in which the objective is to maximize the number of active users when spreading information throughout a social network. Among all the influence maximization variants, TSS introduces the concept of reward of each user, which is the benefit associated to its activation. Therefore, the problem tries to maximize the reward obtained among all active users by selecting an initial set of users. Each user has also associated an activation cost, and the total sum of activation costs of the initial set of selected users cannot exceed a certain budget. In particular, two Path Relinking approaches are proposed, comparing them with the best method found in the state of the art. Additionally, a more challenging set of instances are derived from real-life social networks, where the best previous method is not able to find a feasible solution. The experimental results show the efficiency and efficacy of the proposal, supported by non-parametric statistical tests. & COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords:
Target Set Selection problem
Influence maximization
Dynamic Path Relinking
GRASP
Social networks
Metaheuristics
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