通过边聚类检测时间超图中的交互模式
Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
AI总结：
本文提出通过边聚类方法检测时间超图中交互模式，利用结构相似性函数发现顶点簇以辅助后续任务。
摘要：
在无监督设置下，寻找图中顶点的密集连接子集，称为聚类或社区检测，是网络科学中的基本问题之一。边聚类方法通过聚类图中的边并根据边所属社区中至少有一个边的顶点来分配社区，从而允许顶点的重叠簇。我们将边聚类的思想应用于时间超图，这是一种扩展图，其中单条边可以包含任意数量的顶点，且每条边都有一个时间戳。扩展到超图允许边之间存在多种交互模式，通过定义合适的结构相似性函数，我们的边聚类算法可以找到这些模式的簇。我们在一个大型合作超图上使用三种结构相似性函数测试该算法，并发现直观的簇结构，这些结构可能对后续任务有所帮助。
英文摘要：
Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge clustering approach instead detects communities by clustering the edges of the graph and then assigning a vertex to a community if it has at least one edge in that community, thereby allowing for overlapping clusters of vertices. We apply the idea behind edge clustering to temporal hypergraphs, an extension of a graph where a single edge can contain any number of vertices and each edge has a timestamp. Extending to hypergraphs allows for many different patterns of interaction between edges, and by defining a suitable structural similarity function, our edge clustering algorithm can find clusters of these patterns. We test the algorithm with three structural similarity functions on a large collaboration hypergraph, and find intuitive cluster structures that could prove useful for downstream tasks.
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。
