论文标题
当更少的情况下：基于级联的社区检测的系统分析
When Less Is More: Systematic Analysis of Cascade-Based Community Detection
论文作者
论文摘要
信息扩散，传染病的传播以及谣言的传播是现实生活中发生的基本过程。 In many practical cases, one can observe when nodes become infected, but the underlying network, over which a contagion or information propagates, is hidden. Inferring properties of the underlying network is important since these properties can be used for constraining infections, forecasting, viral marketing, and so on.此外，对于许多应用程序，仅恢复该网络的粗大高级属性而不是所有边缘就足够了。 This article conducts a systematic and extensive analysis of the following problem: Given only the infection times, find communities of highly interconnected nodes.这项任务与研究充分的社区检测问题有显着不同，因为我们没有观察到要聚类的图。 We carry out a thorough comparison between existing and new approaches on several large datasets and cover methodological challenges specific to this problem. One of the main conclusions is that the most stable performance and the most significant improvement on the current state-of-the-art are achieved by our proposed simple heuristic approaches agnostic to a particular graph structure and epidemic model.我们还表明，可以通过基于级联数据包括边缘权重来增强一些著名的社区检测算法。
Information diffusion, spreading of infectious diseases, and spreading of rumors are fundamental processes occurring in real-life networks. In many practical cases, one can observe when nodes become infected, but the underlying network, over which a contagion or information propagates, is hidden. Inferring properties of the underlying network is important since these properties can be used for constraining infections, forecasting, viral marketing, and so on. Moreover, for many applications, it is sufficient to recover only coarse high-level properties of this network rather than all its edges. This article conducts a systematic and extensive analysis of the following problem: Given only the infection times, find communities of highly interconnected nodes. This task significantly differs from the well-studied community detection problem since we do not observe a graph to be clustered. We carry out a thorough comparison between existing and new approaches on several large datasets and cover methodological challenges specific to this problem. One of the main conclusions is that the most stable performance and the most significant improvement on the current state-of-the-art are achieved by our proposed simple heuristic approaches agnostic to a particular graph structure and epidemic model. We also show that some well-known community detection algorithms can be enhanced by including edge weights based on the cascade data.
