AAANE:Attention-Based Adversarial Autoencoder for Multi-scale Network Embedding

摘要：

　　Network embedding represents nodes in a continuous vector space and preserves structure information from a network.Existing methods usually adopt a “one-size-fits-all approach when concerning multi-scale structure information,such as first-and second-order proximity of nodes,ignoring the fact that different scales play different roles in embedding learning.In this paper,we propose an Attention-based Adversarial Autoencoder Network Embedding(AAANE)framework,which promotes the collaboration of different scales and lets them vote for robust representations.The proposed AAANE consists of two components:(1)an attention-based autoencoder that effectively capture the highly non-linear network structure,which can de-emphasize irrelevant scales during training,and(2)an adversarial regularization guides the autoencoder in learning robust representations by matching the posterior distribution of the latent embeddings to a given prior distribution.Experimental results on real-world networks show that the proposed approach outperforms strong baselines.

关键词： Network embedding Multi-scale Attention Adversarial autoencoder

作者: Lei Sang Min Xu Shengsheng Qian Xindong Wu

作者单位: School of Computer Science and Information Technology,Hefei University of Technology,Hefei,China;Fac Faculty of Engineering and IT,University of Technology Sydney,Ultimo,Australia Institute of Automation,Chinese Academy of Sciences,Beijing,China School of Computer Science and Information Technology,Hefei University of Technology,Hefei,China

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 3-14

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）