十亿级网络中影响力动态的重要性刻画
Importance Sketching of Influence Dynamics in Billion-scale Networks
摘要：
社会，生物和通信网络的痕迹的蓬勃发展为分析网络中的扩散过程提供了前所未有的机会。然而，当今网络的庞大规模在计算效率和可扩展性方面提出了严峻的挑战。在本文中，我们提出了一个新的超图素描框架的影响动力学网络。我们的草图框架，称为SKIS的中心，是一个有效的重要性采样算法，只返回网络中的非奇异反向级联。与以前开发的草图，如RIS和SKIM相比，我们的草图显着提高了估计质量，同时大大减少了处理时间和内存占用。此外，我们提出了一般策略，使用SKIS，以提高现有的算法的影响力估计和影响力最大化，这是出于实际应用，如病毒式营销。使用SKIS，我们为种子集设计了高质量的影响预言，平均估计误差比使用RIS的小10倍，比SKIM小6倍。此外，我们的影响力最大化使用SKIS大大提高了贪心算法的解决方案的质量。它为最快的基于RIS的DSSA算法实现了高达10倍的加速和4倍的内存减少，同时保持了相同的理论保证。
英文摘要：
The blooming availability of traces for social, biological, and communication networks opens up unprecedented opportunities in analyzing diffusion processes in networks. However, the sheer sizes of the nowadays networks raise serious challenges in computational efficiency and scalability. In this paper, we propose a new hyper-graph sketching framework for inflence dynamics in networks. The central of our sketching framework, called SKIS, is an efficient importance sampling algorithm that returns only non-singular reverse cascades in the network. Comparing to previously developed sketches like RIS and SKIM, our sketch significantly enhances estimation quality while substantially reducing processing time and memory-footprint. Further, we present general strategies of using SKIS to enhance existing algorithms for influence estimation and influence maximization which are motivated by practical applications like viral marketing. Using SKIS, we design high-quality influence oracle for seed sets with average estimation error up to 10x times smaller than those using RIS and 6x times smaller than SKIM. In addition, our influence maximization using SKIS substantially improves the quality of solutions for greedy algorithms. It achieves up to 10x times speed-up and 4x memory reduction for the fastest RIS-based DSSA algorithm, while maintaining the same theoretical guarantees.
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