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Seeds Buffering for Information Spreading Processes

机译：种子传播过程中的种子缓冲

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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：十多年来，已经研究了社交网络中影响力最大化的播种策略。在开始时，他们主要依靠同时激活所有资源（种子）。然而，已经证明顺序播种策略通常更好。这项研究专注于研究带缓冲的顺序播种，这是对基本顺序播种概念的扩展。所提出的方法避免了选择将通过自然扩散过程激活的节点，这导致在社会影响力过程中更好地利用预算来激活种子节点。将该方法与没有缓冲和单阶段播种的顺序播种进行了比较。真实和人工社交网络上的结果都证实，基于缓冲区的连续播种是最终覆盖率和到达时间之间的良好折衷。在固定预算下，它的表现明显优于竞争对手。通过动态排名和检测尚未激活的节点并具有激活其邻居潜力的节点的网络区域的能力来获得收益。

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