标题： 通过边聚类检测时间超图中的交互模式 显示英文标题

标题： Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering

摘要： 在无监督的设置下寻找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。 边缘聚类方法通过聚类图的边，然后如果一个顶点在一个社区中有至少一条边，则将其分配到该社区，从而允许顶点的重叠聚类来检测社区。 我们将边缘聚类背后的思想应用于时间超图，这是图的一种扩展，其中一条边可以包含任意数量的顶点，并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不同的交互模式，通过定义合适的结构相似性函数，我们的边缘聚类算法可以找到这些模式的聚类。 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了该算法，并发现可能对下游任务有用的直观聚类结构。 显示英文摘要

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.