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Seeds Buffering for Information Spreading Processes

机译：用于信息传播过程的种子缓冲

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Seeding strategies for influence maximization in social networks have been studied for more than a decade. They have mainly relied on the activation of all resources (seeds) simultaneously in the beginning; yet, it has been shown that sequential seeding strategies are commonly better. This research focuses on studying sequential seeding with buffering, which is an extension to basic sequential seeding concept. The proposed method avoids choosing nodes that will be activated through the natural diffusion process, which is leading to better use of the budget for activating seed nodes in the social influence process. This approach was compared with sequential seeding without buffering and single stage seeding. The results on both real and artificial social networks confirm that the buffer-based consecutive seeding is a good trade-off between the final coverage and the time to reach it. It performs significantly better than its rivals for a fixed budget. The gain is obtained by dynamic rankings and the ability to detect network areas with nodes that are not yet activated and have high potential of activating their neighbours.
机译：已经研究了社交网络中影响最大化的播种策略超过了十多年。他们主要依赖于一开始同时激活所有资源（种子）;然而，已经表明，序贯播种策略通常更好。该研究侧重于研究与缓冲的顺序播种，这是基本顺序播种概念的延伸。所提出的方法避免了选择通过自然扩散过程激活的节点，这导致更好地利用在社会影响过程中激活种子节点的预算。将这种方法与连续播种进行比较，而无需缓冲和单级播种。实际和人工社交网络的结果证实，基于缓冲的连续播种是最终覆盖范围和到达时的时间之间的良好权衡。它比固定预算的竞争对手更好地表现得显着更好。通过动态排名获得增益以及检测具有尚未激活的节点的网络区域的网络区域，并且具有激活其邻居的高潜力。

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