Graph embedding, also known as network embedding and network representation learning, is a useful technique which helps researchers analyze information networks through embedding a network into a low-dimensional space. However, existing graph embedding methods are all node-based, which means they can just directly map the nodes of a network to low-dimensional vectors while the edges could only be mapped to vectors indirectly. One important reason is the computational cost, because the number of edges is always far greater than the number of nodes. In this article, considering an important property of social networks, i.e., the network is sparse, and hence the average degree of nodes is bounded, we propose an edge-based graph embedding ( edge2vec ) method to map the edges in social networks directly to low-dimensional vectors. Edge2vec takes both the local and the global structure information of edges into consideration to preserve structure information of embedded edges as much as possible. To achieve this goal, edge2vec first ingeniously combines the deep autoencoder and Skip-gram model through a well-designed deep neural network. The experimental results on different datasets show edge2vec benefits from the direct mapping in preserving the structure information of edges.

中文翻译：

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Edge2vec

图嵌入，也称为网络嵌入和网络表示学习，是一种有用的技术，它通过将网络嵌入到低维空间中来帮助研究人员分析信息网络。然而，现有的图嵌入方法都是基于节点的，这意味着它们只能将网络的节点直接映射到低维向量，而边只能间接映射到向量。一个重要的原因是计算成本，因为边的数量总是远远大于节点的数量。在本文中，考虑到社交网络的一个重要特性，即网络是稀疏的，因此节点的平均度数是有界的，我们提出了一种基于边的图嵌入（边缘2vec) 方法将社交网络中的边缘直接映射到低维向量。Edge2vec同时考虑边缘的局部和全局结构信息，尽可能保留嵌入边缘的结构信息。为了实现这个目标，边缘2vec首先通过精心设计的深度神经网络巧妙地结合了深度自编码器和 Skip-gram 模型。不同数据集上的实验结果表明边缘2vec受益于直接映射在保留边缘的结构信息方面。