标题： 通过高阶模体的超图局部聚类 显示英文标题

标题： Local Clustering in Hypergraphs through Higher-Order Motifs

摘要： 超图提供了一个强大的框架，用于建模具有高阶交互的复杂系统和网络，这些交互超越了简单的成对关系。 然而，主要关注成对关系的基于图的聚类方法无法表示高阶交互，通常会导致低质量的聚类结果。 在本工作中，我们提出了一种基于高阶模体的新方法，用于超图中的局部聚类，这些模体是节点可能通过任何阶次的交互连接的小型连通子图，扩展了之前应用于标准图的基于模体的技术。 我们的方法利用超图特有的高阶模体，更好地表征局部结构并优化模体导通性。 我们提出了两种不同的策略，用于围绕一个种子超边识别局部聚类：一种是利用超图核心分解的核心方法，另一种是基于广度优先探索的方法。 我们构建了一个辅助超图以促进高效的划分，并引入了一个基于局部模体的聚类框架。 在真实数据集上的大量实验表明了我们框架的有效性，并从聚类质量和计算效率的角度对两种提出的聚类策略进行了比较分析。 显示英文摘要

摘要： Hypergraphs provide a powerful framework for modeling complex systems and networks with higher-order interactions beyond simple pairwise relationships. However, graph-based clustering approaches, which focus primarily on pairwise relations, fail to represent higher-order interactions, often resulting in low-quality clustering outcomes. In this work, we introduce a novel approach for local clustering in hypergraphs based on higher-order motifs, small connected subgraphs in which nodes may be linked by interactions of any order, extending motif-based techniques previously applied to standard graphs. Our method exploits hypergraph-specific higher-order motifs to better characterize local structures and optimize motif conductance. We propose two alternative strategies for identifying local clusters around a seed hyperedge: a core-based method utilizing hypergraph core decomposition and a BFS-based method based on breadth-first exploration. We construct an auxiliary hypergraph to facilitate efficient partitioning and introduce a framework for local motif-based clustering. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework and provide a comparative analysis of the two proposed clustering strategies in terms of clustering quality and computational efficiency.