论文标题
将节点结构角色身份嵌入双曲线空间
Embedding Node Structural Role Identity into Hyperbolic Space
论文作者
论文摘要
最近，由于已显示双曲线空间在捕获图/网络结构方面很好地奏效，因此人们对嵌入双曲线空间中的网络产生了兴趣，因为它自然可以反映复杂网络的某些属性。但是，双曲线空间中网络嵌入的工作一直集中在微观节点嵌入上。在这项工作中，我们是第一个提出将节点的结构作用嵌入双曲线空间的框架。我们的框架扩展了通过将其移动到倍曲底模型的结构2VEC，这是一种众所周知的结构作用，保留了嵌入方法。我们在四个现实世界和一个合成网络上评估了我们的方法。我们的结果表明，在学习节点的结构作用的潜在表示方面，双曲线空间比欧几里得空间更有效。
Recently, there has been an interest in embedding networks in hyperbolic space, since hyperbolic space has been shown to work well in capturing graph/network structure as it can naturally reflect some properties of complex networks. However, the work on network embedding in hyperbolic space has been focused on microscopic node embedding. In this work, we are the first to present a framework to embed the structural roles of nodes into hyperbolic space. Our framework extends struct2vec, a well-known structural role preserving embedding method, by moving it to a hyperboloid model. We evaluated our method on four real-world and one synthetic network. Our results show that hyperbolic space is more effective than euclidean space in learning latent representations for the structural role of nodes.
