Many successful methods have been proposed for learning low dimensional representations on large-scale networks, while almost all existing methods are designed in inseparable processes, learning embeddings for entire networks even when only a small proportion of nodes are of interest. This leads to great inconvenience, especially on super-large or dynamic networks, where these methods become almost impossible to implement. In this paper, we formalize the problem of separated matrix factorization, based on which we elaborate a novel objective function that preserves both local and global information. We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes. By implementing separability, our algorithm reduces the redundant efforts to embed irrelevant nodes, yielding scalability to super-large networks, automatic implementation in distributed learning and further adaptations. We demonstrate the effectiveness of this approach on several real-world networks with different scales and subjects. With comparable accuracy, our approach significantly outperforms state-of-the-art baselines in running times on large networks.

本文提出了一种基于矩阵分解的网络嵌入算法，该算法通过实现节点分离来减少不相关节点的嵌入，从而达到大规模网络的可扩展性，适用于分布式学习以及其他进一步适应性，实验结果表明，该算法在保证准确性的前提下，显著提高了大型网络的运行时间，并优于现有的基线算法。

SepNE: 将可分离性引入网络嵌入