论文标题
朝着以边缘为中心的网络嵌入
Toward Edge-Centric Network Embeddings
论文作者
论文摘要
现有的网络嵌入方法解决了学习低维节点表示的问题。但是，也可以从相互联系的节点对的边缘看到网络。本文的广泛目标是引入以边缘为中心的网络嵌入。我们提出了一种称为ecne的方法，该方法不是直接计算节点嵌入，而是通过依靠线图的概念和边缘加权机制来保留线图中原始图的动态来计算边缘嵌入。我们还提出了一个名为Ecne-LP的链接预测框架，该链接框架给定一个目标链接（U，V）首先收集节点U和V之间的路径，然后将边缘直接嵌入这些路径中，最后将它们汇总为预测链接的存在。我们表明，Ecne和Ecne-LP都带来了最先进的福利。
Existing network embedding approaches tackle the problem of learning low-dimensional node representations. However, networks can also be seen in the light of edges interlinking pairs of nodes. The broad goal of this paper is to introduce edge-centric network embeddings. We present an approach called ECNE, which instead of computing node embeddings directly, computes edge embeddings by relying on the notion of line graph coupled with an edge weighting mechanism to preserve the dynamic of the original graph in the line graph. We also present a link prediction framework called ECNE-LP, which given a target link (u,v) first collects paths between nodes u and v, then directly embeds the edges in these paths, and finally aggregates them toward predicting the existence of a link. We show that both ECNE and ECNE-LP bring benefit wrt the state-of-the-art.
