论文标题
在超图中推断超猫和重叠社区
Inference of hyperedges and overlapping communities in hypergraphs
论文作者
论文摘要
在任何数量的系统单元之间编码结构化的相互作用的HyperGraphs最近证明了一种成功描述许多现实世界生物学和社交网络的工具。在这里，我们提出了一个基于统计推断的框架，以表征超图的结构组织。该方法允许以原则性的方式推断出任何大小的丢失的超辫，并在存在高阶相互作用的情况下共同检测重叠的社区。此外，我们的模型具有有效的数值实现，并且比从高阶数据投影的成对记录上的二元算法要快。我们将方法应用于各种现实世界系统，在超边缘预测任务中显示出强烈的性能，检测社区与相互作用所携带的信息很好，并且鲁棒性与添加嘈杂的Hyperedges相符。我们的方法说明了使用高阶相互作用的关系系统进行建模时，超图概率模型的基本优势。
Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on statistical inference to characterize the structural organization of hypergraphs. The method allows to infer missing hyperedges of any size in a principled way, and to jointly detect overlapping communities in presence of higher-order interactions. Furthermore, our model has an efficient numerical implementation, and it runs faster than dyadic algorithms on pairwise records projected from higher-order data. We apply our method to a variety of real-world systems, showing strong performance in hyperedge prediction tasks, detecting communities well aligned with the information carried by interactions, and robustness against addition of noisy hyperedges. Our approach illustrates the fundamental advantages of a hypergraph probabilistic model when modeling relational systems with higher-order interactions.
