The hypergraph community detection problem seeks to identify groups of related nodes in hypergraph data. We propose an information-theoretic hypergraph community detection algorithm which compresses the observed data in terms of community labels and community-edge intersections. This algorithm can also be viewed as maximum-likelihood inference in a degree-corrected microcanonical stochastic blockmodel. We perform the inference/compression step via simulated annealing. Unlike several recent algorithms based on canonical models, our microcanonical algorithm does not require inference of statistical parameters such as node degrees or pairwise group connection rates. Through synthetic experiments, we find that our algorithm succeeds down to recently-conjectured thresholds for sparse random hypergraphs. We also find competitive performance in cluster recovery tasks on several hypergraph data sets.

翻译：超图社区检测问题旨在识别超图数据中相关的节点组。我们提出了一种基于信息论的超图社区检测算法，该算法通过社区标签和社区-边交集的压缩观察数据。该算法也可视为度修正微正则随机块模型下的最大似然推断。我们通过模拟退火执行推断/压缩步骤。与最近基于正则模型的若干算法不同，我们的微正则算法无需推断节点度数或成对群体连接率等统计参数。通过合成实验，我们发现该算法在稀疏随机超图的近期猜想阈值以下仍能成功检测。在多个超图数据集上的簇恢复任务中，我们也观察到竞争性的性能表现。