从扩散网络采样
Sampling from Diffusion Networks
摘要：
这种扩散现象对在线社交网络（OSNs）有着显著的影响。在这些大型网络上收集扩散数据遇到了许多挑战，这些挑战可以通过采用合适的采样方法来缓解。本文的贡献是双重的。首先，我们研究了扩散网络上的采样方法，并首次将这些方法分为两类：（1）基于结构的采样（SBS）和（2）基于扩散的采样（DBS）。前一种方法对网络拓扑特征的依赖性，以及后者中真实扩散路径的不可用性，将选择适当采样方法的问题转化为权衡。其次，我们正式定义了扩散网络采样问题，并提出了一些新的基于扩散的特性来评估引入的采样方法。在大规模模拟数据集和真实数据集上的实验表明，DBS算法在较高的采样率（平均16% ~ 29%）下性能明显优于SBS算法，但在较低的采样率下性能相差约7%.因此，在实际的大规模系统中，低采样率的要求，SBS将是一个更好的选择，根据其较低的时间复杂度在收集数据相比，DBS。此外，我们表明，引入的采样方法（SBS和DBS）发挥更重要的作用比图的探索技术，如宽度优先搜索（BFS）和随机游走（RW）在扩散过程的分析。
英文摘要：
The diffusion phenomenon has a remarkable impact on Online Social Networks (OSNs). Gathering diffusion data over these large networks encounters many challenges which can be alleviated by adopting a suitable sampling approach. The contributions of this paper is twofold. First we study the sampling approaches over diffusion networks, and for the first time, classify these approaches into two categories; (1) Structure-based Sampling (SBS), and (2) Diffusion-based Sampling (DBS). The dependency of the former approach to topological features of the network, and unavailability of real diffusion paths in the latter, converts the problem of choosing an appropriate sampling approach to a trade-off. Second, we formally define the diffusion network sampling problem and propose a number of new diffusion-based characteristics to evaluate introduced sampling approaches. Our experiments on large scale synthetic and real datasets show that although DBS performs much better than SBS in higher sampling rates (16% ~ 29% on average), their performances differ about 7% in lower sampling rates. Therefore, in real large scale systems with low sampling rate requirements, SBS would be a better choice according to its lower time complexity in gathering data compared to DBS. Moreover, we show that the introduced sampling approaches (SBS and DBS) play a more important role than the graph exploration techniques such as Breadth-First Search (BFS) and Random Walk (RW) in the analysis of diffusion processes.
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