We study the min-cost seed selection problem in online social networks for viral marketing, where the goal is to select a set of seed nodes with the minimum total cost such that the expected number of influenced nodes in the network exceeds a predefined threshold. We propose several algorithms that outperform the previous studies both on the theoretical approximation ratio and on the experimental performance. In the case where the nodes have heterogeneous costs, our algorithms are the first bi-criteria approximation algorithms with polynomial running time and provable approximation ratio. In the case where the users have uniform costs, our algorithms achieve logarithmic approximation ratio and provable time complexity which is smaller than that of the existing algorithms in orders of magnitude. We conduct extensive experiments using real social networks. The experimental results show that, our algorithms significantly outperform the existing algorithms both on the total cost and on the running time, and also scale well to billion-scale networks.

中文翻译：

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物超所值：经济高效的在线社交网络种子选择

我们研究了病毒式营销在线社交网络中的最小成本种子选择问题，其目标是选择一组具有最小总成本的种子节点，使得网络中受影响节点的预期数量超过预定义的阈值。我们提出了几种算法，它们在理论近似比和实验性能方面都优于先前的研究。在节点具有异构成本的情况下，我们的算法是第一个具有多项式运行时间和可证明逼近比的双准则逼近算法。在用户具有统一成本的情况下，我们的算法实现了对数逼近比和可证明的时间复杂度，比现有算法小几个数量级。我们使用真实的社交网络进行了广泛的实验。实验结果表明，我们的算法在总成本和运行时间上都明显优于现有算法，并且可以很好地扩展到十亿级网络。