文献类型：期刊文献

英文题名：A representation learning model based on stochastic perturbation and homophily constraint

作者：Li, Qi[1];Jiang, Ming[2]

机构：[1]Shaoxing Univ, Inst Artificial Intelligence, Shaoxing 213000, Zhejiang, Peoples R China;[2]Guangxi Inst Digital Technol Co Ltd, Nanning 530022, Guangxi, Peoples R China

年份：2023

外文期刊名：KNOWLEDGE AND INFORMATION SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:001034200400001)、、EI(收录号：20233014449466)、Scopus(收录号：2-s2.0-85165641168)、WOS

基金：AcknowledgementsThis work was supported by Natural Sciences Foundation of Zhejiang Province under Grant No. LY22F020003, National Natural Science Foundation of China under Grant No. 62002226 and China Postdoctoral Science Foundation under Grant No. 2022M711715.

语种：英文

外文关键词：Representation learning; Complex network; Deep learning; Semantic information

外文摘要：The network representation learning task of fusing node multi-dimensional classification information aims to effectively combine node multi-dimensional classification information and network structure information for representation learning, thereby improving the performance of network representation. However, the existing methods only consider multi-dimensional classification information as priori features, which assists the representation learning of the network structure information, lacks the coping mechanism in the case of missing data, and have low robustness in the case of incomplete information. To address these issues, in this paper, we propose a representation learning model based on stochastic perturbation and homophily constraint, called IMCIN. On the one hand, the data transformation is carried out through the random perturbation strategy to improve the adaptability of the model to incomplete information. On the other hand, in the process of learning fusion representation vectors, an attribute similarity retention method based on the principle of homogeneity is designed to further mine the effective semantic information in the incomplete information. Experiments show that our method can effectively deal with the problem of incomplete information and improve the performance of node classification and link prediction tasks.