High-order interaction events are common in real-world applications. Learning embeddings that encode the complex relationships of the participants from these events is of great importance in knowledge mining and predictive tasks. Despite the success of existing approaches, e.g. Poisson tensor factorization, they ignore the sparse structure underlying the data, namely the occurred interactions are far less than the possible interactions among all the participants. In this paper, we propose Nonparametric Embeddings of Sparse High-order interaction events (NESH). We hybridize a sparse hypergraph (tensor) process and a matrix Gaussian process to capture both the asymptotic structural sparsity within the interactions and nonlinear temporal relationships between the participants. We prove strong asymptotic bounds (including both a lower and an upper bound) of the sparsity ratio, which reveals the asymptotic properties of the sampled structure. We use batch-normalization, stick-breaking construction, and sparse variational GP approximations to develop an efficient, scalable model inference algorithm. We demonstrate the advantage of our approach in several real-world applications.
翻译:在现实世界应用中,高层次互动事件很常见。学习嵌入这些事件参与者复杂关系的嵌入过程在知识挖掘和预测任务中非常重要。尽管现有方法取得了成功,例如Poisson Excisticalization,但是它们忽略了数据背后的稀少结构,即发生的相互作用远远少于所有参与者之间可能的相互作用。在本文中,我们建议采用非对称嵌入斯普鲁斯高层次互动事件(NESH)。我们混合了一个稀有的超强(超强)进程和一个矩阵Gaussian进程,以捕捉参与者之间互动和非线性时间关系中的无症状结构孔径。我们证明,在几个现实世界中,我们的方法具有优势。