论文标题
神经图嵌入作为链接预测的显式低级矩阵分解
Neural graph embeddings as explicit low-rank matrix factorization for link prediction
论文作者
论文摘要
长期以来，通过在模拟随机步行中将点的互信息（PMI）最大程度地减少了点的相互信息（PMI），从而实现了高质量的神经图嵌入。这种设计选择主要是通过直接应用嵌入算法Word2Vec的直接应用来预测社会，共同引文和生物网络中新链接的形成的。但是，这种图形嵌入方法的Skeuomormormormormormormormormormormormormormormormormormormormormormormormorphic需要从PMI较低的一对节点发出的信息截断。为了避免此问题，我们提出了一种改进的方法来学习低级分解嵌入，这些方法包括从这种不太可能的节点对的信息，并表明它可以提高基线方法的链接预测性能从1.2％到24.2％。根据我们的结果和观察，我们概述了进一步的步骤，这些步骤可以改善基于基质分解的下一个图形嵌入算法的设计。
Learning good quality neural graph embeddings has long been achieved by minimizing the point-wise mutual information (PMI) for co-occurring nodes in simulated random walks. This design choice has been mostly popularized by the direct application of the highly-successful word embedding algorithm word2vec to predicting the formation of new links in social, co-citation, and biological networks. However, such a skeuomorphic design of graph embedding methods entails a truncation of information coming from pairs of nodes with low PMI. To circumvent this issue, we propose an improved approach to learning low-rank factorization embeddings that incorporate information from such unlikely pairs of nodes and show that it can improve the link prediction performance of baseline methods from 1.2% to 24.2%. Based on our results and observations we outline further steps that could improve the design of next graph embedding algorithms that are based on matrix factorization.
