This paper studies the problem of embedding very large information networks
into low-dimensional vector spaces, which is useful in many tasks such as
visualization, node classification, and link prediction. Most existing graph
embedding methods do not scale for real world information networks which
usually contain millions of nodes. In this paper, we propose a novel network
embedding method called the "LINE," which is suitable for arbitrary types of
information networks: undirected, directed, and/or weighted. The method
optimizes a carefully designed objective function that preserves both the local
and global network structures. An edge-sampling algorithm is proposed that
addresses the limitation of the classical stochastic gradient descent and
improves both the effectiveness and the efficiency of the inference. Empirical
experiments prove the effectiveness of the LINE on a variety of real-world
information networks, including language networks, social networks, and
citation networks. The algorithm is very efficient, which is able to learn the
embedding of a network with millions of vertices and billions of edges in a few
hours on a typical single machine. The source code of the LINE is available
online.

本文提出了一种新的网络嵌入方法 LINE，用于将大规模的信息网络嵌入到低维向量空间中，实现了节点分类，链路预测等任务。该方法优化了一个经过精心设计的目标函数，可以保留本地和全局网络结构，并提出了边缘采样算法，改进了经典随机梯度下降的限制和推理效率。经验证明了 LINE 在包括语言网络、社交网络和引用网络在内的各种真实世界的信息网络上的有效性。对于单个典型机器，该算法非常高效，可以在数小时内学习具有数百万个顶点和数十亿个边的网络嵌入。