计算机工程与应用2019，Vol.55Issue(12)：117-123,1,8.DOI:10.3778/j.issn.1002-8331.1812-0079
半监督属性网络表示学习方法
Semi-Supervised Representation Learning Method for Attributed Networks
摘要
Abstract
Network representation learning is an important research topic, which purpose is to represent high-dimensional attribute networks as low-dimensional dense vectors, so as to provide effective feature representation for the next task. SNE, a recently proposed attribute network representation learning model, uses both network structure and attribute infor-mation to represent learning network nodes. However, this model belongs to the unsupervised model and cannot make full use of some easily acquired prior information to improve the quality of the learned feature representation. Based on the above considerations, a semi-supervised attribute network representation learning method SSNE is proposed, which takes the attribute network and a small number of node priors as input of the feedforward neural network, learning the optimal node representation by maintaining the network link structure and a small number of node priors in the output layer through multiple hidden layer nonlinear transformations. Compared with the existing mainstream methods on four real attri-bute networks and two artificial attribute networks, the results show that the representation learned in this method has bet-ter performance in clustering and classification tasks.关键词
属性网络/半监督学习/表示学习/聚类Key words
attribute networks/ semi-supervised learning/ representation learning/ clustering分类
信息技术与安全科学引用本文复制引用
张璞,柴变芳,张静,李文斌..半监督属性网络表示学习方法[J].计算机工程与应用,2019,55(12):117-123,1,8.基金项目
国家自然科学基金(No.61503260) （No.61503260）
河北省科技厅软科学研究计划项目(No.17456001D) （No.17456001D）
河北省研究生创新资助项目(No.CXZZSS2018118) （No.CXZZSS2018118）
河北省高等学校科学技术研究项目(No.ZD2018043). （No.ZD2018043）
