一种基于普查的遗传算法用于社交网络中的目标集选择问题
A Census-Based Genetic Algorithm for Target Set Selection Problem in Social Networks
AI总结：
本文针对社交网络目标集选择问题提出一种基于普查的遗传算法，通过收集个体和节点出现频次信息增强多样性并避免早熟收敛，支持并行化和自适应调节，在随机图及14个真实大规模社交网络上取得优于已有最佳解的结果。
摘要：
本文研究社交网络中的目标集选择（TSS）问题，这是病毒式营销中的一个基本问题。在TSS问题中，给定一个图以及图中每个顶点的阈值。我们需要找到一个最小规模的顶点子集来“激活”，使得在传播过程结束时所有图顶点都被激活。具体来说，我们提出了一种称为“基于普查的遗传算法”的新方法来解决TSS问题。在我们的算法中，我们利用普查的思想来收集和存储种群中每个个体的信息，并在算法执行过程中从构造的个体中收集普查数据，从而能够实现更大的多样性，避免过早收敛到局部最优解。我们使用两种不同的普查信息：（a）对于每个个体，算法存储其在执行过程中被识别出的次数；（b）对于每个网络节点，算法统计其被包含在解中的次数。所提出的算法还可以通过一个参数进行自我调整，该参数指定每种繁殖方法中采用的激进程度。此外，该算法被设计为在并行化环境中运行，以最小化计算成本并检查每个个体的可行性。而且，我们的算法在随机图上的实验中在所有情况下都找到了最优解。此外，我们在文献中的14个真实社交网络实例大图上执行了所提出的算法，与先前研究中获得的最佳解相比，平均改进了约9.57的解规模，总共改进了134个顶点。
英文摘要：
This paper considers the Target Set Selection (TSS) Problem in social networks, a fundamental problem in viral marketing. In the TSS problem, a graph and a threshold value for each vertex of the graph are given. We need to find a minimum size vertex subset to "activate" such that all graph vertices are activated at the end of the propagation process. Specifically, we propose a novel approach called "a census-based genetic algorithm" for the TSS problem. In our algorithm, we use the idea of a census to gather and store information about each individual in a population and collect census data from the individuals constructed during the algorithm's execution so that we can achieve greater diversity and avoid premature convergence at locally optimal solutions. We use two distinct census information: (a) for each individual, the algorithm stores how many times it has been identified during the execution (b) for each network node, the algorithm counts how many times it has been included in a solution. The proposed algorithm can also self-adjust by using a parameter specifying the aggressiveness employed in each reproduction method. Additionally, the algorithm is designed to run in a parallelized environment to minimize the computational cost and check each individual's feasibility. Moreover, our algorithm finds the optimal solution in all cases while experimenting on random graphs. Furthermore, we execute the proposed algorithm on 14 large graphs of real-life social network instances from the literature, improving around 9.57 solution size (on average) and 134 vertices (in total) compared to the best solutions obtained in previous studies.
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