引用Deep Walk論文中的摘要部分：
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, 學習節點隱表達，在一個連續向量空間中對節點的社會關係進行編碼，是語言模型和無監督學習從單詞序列到圖上的一個擴展。該方法將截斷遊走的序列當成句子進行學習。該方法具有可擴展，可並行化的特點，可以用來做網絡分類和異常點檢測。
Deep Walk，隨機遊走遍歷某節點的鄰節點，得到一個節點序列，再借鑑skip-gram的原理，由單個節點預測前後序列，學習得到該節點的向量表示
算法流程圖：
