标题： 通过超边复制的超图链接预测 显示英文标题

标题： Hypergraph Link Prediction via Hyperedge Copying

摘要： 我们提出了一种时间演化的超图生成模型，其中超边通过复制之前的超边而形成。 我们的模型能够再现许多经验超图中的几种典型事实，可以从数据中学习，并在完整的超图上定义似然，而不是基于自我的或其他子超图。 分析我们的模型，我们得出了节点度、边大小和边交集大小分布的描述，这些分布是根据模型参数得出的。 我们还展示了经验超图的一些特征，这些特征是或不是被我们的模型成功捕捉的。 我们提供了一个可扩展的随机期望最大化算法，可以将我们的模型拟合到包含数百万个节点和边的超图数据集上。 最后，我们在超图链接预测任务上评估了我们的模型，发现仅使用11个参数的模型实例就可以与大型神经网络达到具有竞争力的预测性能。 显示英文摘要

摘要： We propose a generative model of temporally-evolving hypergraphs in which hyperedges form via noisy copying of previous hyperedges. Our proposed model reproduces several stylized facts from many empirical hypergraphs, is learnable from data, and defines a likelihood over a complete hypergraph rather than ego-based or other sub-hypergraphs. Analyzing our model, we derive descriptions of node degree, edge size, and edge intersection size distributions in terms of the model parameters. We also show several features of empirical hypergraphs which are and are not successfully captured by our model. We provide a scalable stochastic expectation maximization algorithm with which we can fit our model to hypergraph data sets with millions of nodes and edges. Finally, we assess our model on a hypergraph link prediction task, finding that an instantiation of our model with just 11 parameters can achieve competitive predictive performance with large neural networks.