论文标题
Epine：增强的接近信息网络嵌入
EPINE: Enhanced Proximity Information Network Embedding
论文作者
论文摘要
无监督的均匀网络嵌入（NE）将网络的每个顶点代表到低维矢量中，同时保留网络信息。邻接矩阵保留大多数网络信息，并直接使一阶接近性符合特征。在这项工作中，我们致力于在更深层次的邻接矩阵中挖掘有价值的信息。在相同的目标下，许多NE方法通过邻接矩阵的能力计算高阶接近性，这是不准确且精心设计的。取而代之的是，我们建议以更直观的方式重新定义高阶接近度。此外，我们设计了一种用于计算的新型算法，该算法减轻了高阶接近准确计算领域中的可伸缩性问题。对现实世界网络数据集的全面实验证明了我们方法在下游机器学习任务中的有效性，例如网络重建，链接预测和节点分类。
Unsupervised homogeneous network embedding (NE) represents every vertex of networks into a low-dimensional vector and meanwhile preserves the network information. Adjacency matrices retain most of the network information, and directly charactrize the first-order proximity. In this work, we devote to mining valuable information in adjacency matrices at a deeper level. Under the same objective, many NE methods calculate high-order proximity by the powers of adjacency matrices, which is not accurate and well-designed enough. Instead, we propose to redefine high-order proximity in a more intuitive manner. Besides, we design a novel algorithm for calculation, which alleviates the scalability problem in the field of accurate calculation for high-order proximity. Comprehensive experiments on real-world network datasets demonstrate the effectiveness of our method in downstream machine learning tasks such as network reconstruction, link prediction and node classification.
