论文标题
来自嘈杂的成对观测的超图重建
Hypergraph reconstruction from noisy pairwise observations
论文作者
论文摘要
网络重建任务旨在从各种数据源（例如时间序列，快照或交互计数）中估算复杂系统的结构。最近的工作检查了网络中的这个问题，其关系涉及两个实体 - 成对情况。在这里，我们研究了重建网络的一般问题，其中还存在高阶交互。我们研究了这个问题的最小例子，重点是与顶点对之间的相互作用和三胞胎之间的相互作用的情况，这些情况是不完美和间接测量的。我们为该模型得出了一个与GIBBS算法的大都市杂货店，并使用算法突出了估计高阶模型带来的独特挑战。我们表明，这种方法倾向于比没有高阶相互作用的等效图模型更准确地重建经验和合成网络。
The network reconstruction task aims to estimate a complex system's structure from various data sources such as time series, snapshots, or interaction counts. Recent work has examined this problem in networks whose relationships involve precisely two entities-the pairwise case. Here we investigate the general problem of reconstructing a network in which higher-order interactions are also present. We study a minimal example of this problem, focusing on the case of hypergraphs with interactions between pairs and triplets of vertices, measured imperfectly and indirectly. We derive a Metropolis-Hastings-within-Gibbs algorithm for this model and use the algorithms to highlight the unique challenges that come with estimating higher-order models. We show that this approach tends to reconstruct empirical and synthetic networks more accurately than an equivalent graph model without higher-order interactions.
