RCELF：一种基于残差的影响力最大化方法
RCELF: A Residual-based Approach for Influence Maximization Problem
摘要：
影响力最大化问题（IMP）是在社会网络中选择一组种子节点，以尽可能广泛地传播影响力。它在多个领域有许多应用，例如，病毒式营销经常用于新产品或活动广告。虽然它是计算机科学中一个经典且研究得很好的问题，但不幸的是，所有这些提出的技术都在时间效率、内存消耗和结果质量之间进行了妥协。在本文中，我们进行全面的实验研究，最先进的IMP近似方法，以揭示潜在的权衡策略。有趣的是，我们发现，即使是国家的最先进的方法是不切实际的，当网络的传播概率已被考虑在内。根据现有方法的研究结果，我们提出了一种新的基于残差的方法（即，RCELF）的IMP，它i）克服了现有的近似方法的不足，和ii）提供了理论保证的结果，在时间和空间的角度都具有高效率。我们通过对真实数据集的广泛实验评估证明了我们的建议的优越性。
英文摘要：
Influence Maximization Problem (IMP) is selecting a seed set of nodes in the social network to spread the influence as widely as possible. It has many applications in multiple domains, e.g., viral marketing is frequently used for new products or activities advertisements. While it is a classic and well-studied problem in computer science, unfortunately, all those proposed techniques are compromising among time efficiency, memory consumption, and result quality. In this paper, we conduct comprehensive experimental studies on the state-of-the-art IMP approximate approaches to reveal the underlying trade-off strategies. Interestingly, we find that even the state-of-the-art approaches are impractical when the propagation probability of the network have been taken into consideration. With the findings of existing approaches, we propose a novel residual-based approach (i.e., RCELF) for IMP, which i) overcomes the deficiencies of existing approximate approaches, and ii) provides theoretical guaranteed results with high efficiency in both time- and space- perspectives. We demonstrate the superiority of our proposal by extensive experimental evaluation on real datasets.
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