融合链接预测相似度矩阵的属性网络嵌入算法

在属性网络中，与节点相关联的属性信息有助于提升网络嵌入各种任务的性能，但网络是一种图状结构，节点不仅包含属性信息还隐含着丰富的结构信息。为了充分融合结构信息，首先通过定义节点的影响力特性、空间关系特征；然后根据链接预测领域基于相似度的定义构建相似度矩阵，将节点二元组中的关联向量映射到相似度矩阵这一关系空间中，从而保留与节点相关的结构向量信息；再基于图的拉普拉斯矩阵融合属性信息和标签特征，将上述三类信息集成到一个最优化框架中；最后，通过二阶导数求局部最大值计算投影矩阵获取节点的特征表示进行网络嵌入。实验结果表明，提出的算法能够充分利用节点二元组的邻接结构信息，相比于其他基准网络嵌入算法，本模型在节点分类任务上取得了更好的结果。

Preserving link prediction similarity matrix for attribute network embedding

In attribute network, the attribute information associated with nodes is essential to improve the performance of network embedding tasks. Nevertheless, network is a graph structure, in which nodes not only contain attribute information but also embrace the rich structural information. In order to make full use of the structural information, firstly, this paper defined influential node characteristics, spatial relationships, and constructed similarity matrix based on the definition of link prediction. Then it mapped correlation similarity vector associated with nodes in the binary group to the relationship space of the adjacency matrix, so as to maintain the node vector matrix structure information feature. Based on the definition of normalized graph Laplacian, it fused the attribute information and label feature and integrated the above three kinds of information into an optimization framework. Finally, it inferenced the projection matrix by calculating the local maximum value through a second order derivative. Experimental results indicate that the proposed algorithm can effectively utilize information of the adjacency structure with the binary group of nodes, and compared with other benchmark network embedding algorithms, it also can achieve better results on the node classification task.