引用Deep Walk论文中的摘要部分：
We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk’s latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk’s representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk’s representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.
Deep Walk, 学习节点隐表达，在一个连续向量空间中对节点的社会关系进行编码，是语言模型和无监督学习从单词序列到图上的一个扩展。该方法将截断游走的序列当成句子进行学习。该方法具有可扩展，可并行化的特点，可以用来做网络分类和异常点检测。
Deep Walk，随机游走遍历某节点的邻节点，得到一个节点序列，再借鉴skip-gram的原理，由单个节点预测前后序列，学习得到该节点的向量表示
算法流程图：
