Network embedding which aims to learn the low-dimensional representations for vertices in networks has been extensively studied in recent years. Although there are various models designed for networks with different properties and different structures for different tasks, most of them are only applied to normal networks which only contain pairwise relationships between vertices. In many realistic cases, relationships among objects are not pairwise and such relationships can be better modeled by a hyper-network in which each edge can connect an uncertain number of vertices. In this article, we focus on two properties of hyper-networks: nonuniform and dual property. In order to make full use of these two properties, we firstly propose a flexible model called Hyper2vec to learn the embeddings of hyper-networks by applying a biased second order random walk strategy to hyper-networks in the framework of Skip-gram. Then, we combine the features of hyperedges by considering the dual hyper-networks to build a further model called NHNE based on 1D convolutional neural networks, and train a tuplewise similarity function for the nonuniform relationships in hyper-networks. Extensive experiments demonstrate the significant effectiveness of our methods for hyper-network embedding.

中文翻译：

---

具有双重机制的非均匀超网络嵌入

近年来，旨在学习网络中顶点的低维表示的网络嵌入得到了广泛的研究。尽管针对不同任务的不同属性和不同结构的网络设计了各种模型，但大多数仅适用于仅包含顶点之间成对关系的普通网络。在许多实际情况下，对象之间的关系不是成对的，这种关系可以通过超网络更好地建模，其中每条边可以连接不确定数量的顶点。在本文中，我们关注超网络的两个属性：非均匀属性和对偶属性。为了充分利用这两个属性，我们首先提出了一种称为 Hyper2vec 的灵活模型，通过在 Skip-gram 框架中对超网络应用有偏二阶随机游走策略来学习超网络的嵌入。然后，我们通过考虑对偶超网络结合超边的特征，进一步构建了一个基于一维卷积神经网络的模型，称为 NHNE，并针对超网络中的非均匀关系训练一个元组相似度函数。大量实验证明了我们的超网络嵌入方法的显着有效性。并为超网络中的非均匀关系训练一个元组相似度函数。大量实验证明了我们的超网络嵌入方法的显着有效性。并为超网络中的非均匀关系训练一个元组相似度函数。大量实验证明了我们的超网络嵌入方法的显着有效性。