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 advertisement. 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.

影响最大化问题 ( IMP ) 是在社交网络中选择一组种子节点以尽可能广泛地传播影响。它在多个领域有很多应用，例如病毒式营销经常用于新产品或活动广告。虽然这是计算机科学中一个经典且经过充分研究的问题，但不幸的是，所有这些提议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，我们对最先进的IMP进行了全面的实验研究揭示潜在权衡策略的近似方法。有趣的是，我们发现当考虑到网络的传播概率时，即使是最先进的方法也是不切实际的。根据现有方法的发现，我们为IMP提出了一种新的基于残差的方法（即RCELF），它 (i) 克服了现有近似方法的不足，并且 (ii) 提供了具有高效率的理论保证结果 -和空间透视。我们通过对真实数据集的广泛实验评估证明了我们提议的优越性。