基于全局注意力机制的属性网络表示学习  

Attribute Network Representation Learning Based on Global Attention

作　　者：许营坤 马放南 杨旭华[1] 叶蕾[1] XU Ying-kun;MA Fang nan;YANG Xu-hua;YE Lei(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023

出　　处：《计算机科学》2021年第12期188-194,共7页Computer Science

基　　金：国家自然科学基金项目(61773348);浙江省自然科学基金(LY20F020029)。

摘　　要：属性网络不仅具有复杂的拓扑结构,其节点还包含丰富的属性信息。属性网络表示学习方法同时提取网络拓扑结构和节点的属性信息来学习大型属性网络的低维向量表示,在节点分类、链路预测和社区识别等网络分析技术方面具有非常重要和广泛的应用。文中首先根据属性网络的拓扑结构得到网络的结构嵌入向量;接着通过全局注意力机制来学习相邻节点的属性信息,先用卷积神经网络对节点的属性信息作卷积操作得到隐藏向量,再对卷积的隐藏向量生成全局注意力的权重向量和相关性矩阵,进而得到节点的属性嵌入向量;最后将结构嵌入向量和属性嵌入向量连接得到同时反映网络结构和节点属性的联合嵌入向量。在3个真实数据集上,将提出的新算法与当前的8种知名网络表示学习模型在链路预测和节点分类等任务上进行比较,实验结果表明新算法具有良好的属性网络表示效果。The attribute network not only has complex topology, its nodes also contain rich attribute information.Attribute network represent learning methods simultaneously extracts network topology and node attribute information to learn low-dimensional vector embedding of large attribute networks.It has very important and extensive applications in network analysis techniques such as node classification, link prediction and community identification.In this paper, we first obtain the embedded vector of the network structure according to the topology of the attribute network.Then we propose to learn the attribute information of adjacent nodes through global attention mechanism, use convolutional neural network to convolve the attribute information of the node to obtain the hidden vectors, and the weight vector and correlation matrix of global attention are generated from the hidden vectors of convolution, and then the attribute embedding vector of nodes is obtained.Finally, the structure embedding vector and the attribute embedding vector are connected to obtain the joint embedding vector which reflects the network structure and the attribute simultaneously.On three real data sets, the new algorithm is compared with the current eight network embedding models on tasks such as link prediction and node classification.Experimental results show that the new algorithm has good attribute network embedding effects.

关 键 词：全局注意力 卷积神经网络 结构嵌入 属性嵌入 联合嵌入 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]