论文标题
在有向图中的聚类边缘
Clustering Edges in Directed Graphs
论文作者
论文摘要
顶点如何在图形数据中发挥影响？我们开发了一个用于边缘聚类的框架，这是一种用于探索性数据分析的新方法，揭示了如何在图形中协作实现定向影响，尤其是针对有向图。与无处不在的顶点聚类相反，哪些顶点，边缘聚类组边缘。共享功能亲和力的边缘分配给同一组并形成影响子图集群。该框架的复杂性与顶点聚类的复杂性相当，为边缘光谱聚类提供了三种不同的方法，这些方法揭示了图形数据中重要的影响子图，每种方法都提供了对有向影响过程的不同见解。我们提出了几种不同的例子，这些例子表明了在科学研究中广泛应用边缘聚类的潜力。
How do vertices exert influence in graph data? We develop a framework for edge clustering, a new method for exploratory data analysis that reveals how both vertices and edges collaboratively accomplish directed influence in graphs, especially for directed graphs. In contrast to the ubiquitous vertex clustering which groups vertices, edge clustering groups edges. Edges sharing a functional affinity are assigned to the same group and form an influence subgraph cluster. With a complexity comparable to that of vertex clustering, this framework presents three different methods for edge spectral clustering that reveal important influence subgraphs in graph data, with each method providing different insight into directed influence processes. We present several diverse examples demonstrating the potential for widespread application of edge clustering in scientific research.
