论文标题
SSNE：稀疏网络中链接预测的有效节点表示
SSNE: Effective Node Representation for Link Prediction in Sparse Networks
论文作者
论文摘要
图形嵌入在复杂网络中的链接预测并实现出色的性能方面广受欢迎。但是，在代表大多数真实网络的稀疏网络中完成了有限的工作。在本文中，我们提出了一个模型，稀疏的结构网络嵌入（SSNE），以获取稀疏网络中链路谓词的节点表示。 SSNE首先将邻接矩阵转换为标准化的$ h $ ordor usvicency ausprix（snham）的总和，然后通过神经网络模型将SNHAM矩阵映射到节点表示的$ d $ d $二维功能矩阵中。事实证明，映射操作是单数值分解的等效变化。最后，我们根据此特征矩阵计算链接预测的节点相似性。通过对合成和实际稀疏网络的广泛测试实验碱基，我们表明该方法提出了更好的链接预测性能，而不是结构相似性索引，矩阵优化和其他图形嵌入模型的链接预测性能。
Graph embedding is gaining its popularity for link prediction in complex networks and achieving excellent performance. However, limited work has been done in sparse networks that represent most of real networks. In this paper, we propose a model, Sparse Structural Network Embedding (SSNE), to obtain node representation for link predication in sparse networks. The SSNE first transforms the adjacency matrix into the Sum of Normalized $H$-order Adjacency Matrix (SNHAM), and then maps the SNHAM matrix into a $d$-dimensional feature matrix for node representation via a neural network model. The mapping operation is proved to be an equivalent variation of singular value decomposition. Finally, we calculate nodal similarities for link prediction based on such feature matrix. By extensive testing experiments bases on synthetic and real sparse network, we show that the proposed method presents better link prediction performance in comparison of those of structural similarity indexes, matrix optimization and other graph embedding models.
