ComPAS：流图的社区保持采样
ComPAS: Community Preserving Sampling for Streaming Graphs
摘要：
在大数据时代，图形采样在许多设置中不可或缺。现有的抽样方法大多是针对静态图设计的，目的是保持原始图的基本结构性质（如度分布、聚类系数等）。在样品中。我们认为，对于任何采样方法，它是不可能产生一个普遍的代表性的样本，可以保持所有的原始图形的属性，而采样应该是特定的应用程序（如保存枢纽-信息扩散所需）。在这里，我们考虑社区发现作为一个应用场景。我们提出了ComPAS，一种新的采样策略，与以前的方法不同，不仅是为流图（这是一个更现实的现实世界的情况下表示），但也保留了社区结构的原始图中的样本。综合和不同的现实世界的图的实证结果表明，ComPAS是最好的保持底层社区结构的平均性能达到73.2%的静态图的最知情的算法。
英文摘要：
In the era of big data, graph sampling is indispensable in many settings. Existing sampling methods are mostly designed for static graphs, and aim to preserve basic structural properties of the original graph (such as degree distribution, clustering coefficient etc.) in the sample. We argue that for any sampling method it is impossible to produce an universal representative sample which can preserve all the properties of the original graph; rather sampling should be application specific (such as preserving hubs - needed for information diffusion). Here we consider community detection as an application scenario. We propose ComPAS, a novel sampling strategy that unlike previous methods, is not only designed for streaming graphs (which is a more realistic representation of a real-world scenario) but also preserves the community structure of the original graph in the sample. Empirical results on both synthetic and different real-world graphs show that ComPAS is the best to preserve the underlying community structure with average performance reaching 73.2% of the most informed algorithm for static graphs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
