论文标题
使用光谱偏见的随机步行在图上的学习表示形式
Learning Representations using Spectral-Biased Random Walks on Graphs
论文作者
论文摘要
几种最先进的神经图嵌入方法是基于简短的随机步行（随机过程），因为它们易于计算，捕获复杂的局部图属性，可扩展性和解释性的简单性。在这项工作中，我们有兴趣研究此随机过程中的概率偏差有多少会影响该过程选择的节点的质量。特别是，我们有偏见的步行，具有一定的概率，有利于向邻居的节点运动与当前节点的社区具有结构相似之处。我们根据节点的邻域子图的频谱（表示为归一化的拉普拉斯矩阵）简洁地捕获该邻居作为概率度量。我们提出使用新的Wasserstein正则化项的段落矢量模型。我们通过经验评估了我们在各种各样的现实数据集上的几种最先进的节点嵌入技术，并证明我们提出的方法在链接预测和节点分类任务上都大大改进了我们所提出的方法。
Several state-of-the-art neural graph embedding methods are based on short random walks (stochastic processes) because of their ease of computation, simplicity in capturing complex local graph properties, scalability, and interpretibility. In this work, we are interested in studying how much a probabilistic bias in this stochastic process affects the quality of the nodes picked by the process. In particular, our biased walk, with a certain probability, favors movement towards nodes whose neighborhoods bear a structural resemblance to the current node's neighborhood. We succinctly capture this neighborhood as a probability measure based on the spectrum of the node's neighborhood subgraph represented as a normalized laplacian matrix. We propose the use of a paragraph vector model with a novel Wasserstein regularization term. We empirically evaluate our approach against several state-of-the-art node embedding techniques on a wide variety of real-world datasets and demonstrate that our proposed method significantly improves upon existing methods on both link prediction and node classification tasks.
