Unsupervised social network embedding via adaptive specific mappings  

作　　者：Youming GE Cong HUANG Yubao LIU Sen ZHANG Weiyang KONG 

机构地区：[1]School of Computer Science and Engineering,Sun Yat-Sen University,Guangzhou 510275,China [2]Guangdong Key Laboratory of Big Data Analysis and Processing,Guangzhou 510006,China

出　　处：《Frontiers of Computer Science》2024年第3期61-71,共11页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.61572537,U1501252).

摘　　要：In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional space.In recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction performance.However,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network embedding.To this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder framework.In encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network structure.In particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network structure.By the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,respectively.Then,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node embedding.In encoder,we adopt the component of reconstruction for the training process of learning node attributes and network structure.We conducted a set of experiments on seven real-world social network datasets.The experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

关 键 词：network embedding specific kernel mapping attention mechanism 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]