.png){kind=icon}
返回
Heterogeneous-attributes enhancement deep framework for network embedding
.png){kind=icon}
PRE
AI
DOI：10.1007/s11704-021-9515-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Network embedding, which targets at learning the vector representation of vertices, has become a crucial issue in network analysis. However, considering the complex structures and heterogeneous attributes in real-world networks, existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity. Thus, more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information. To that end, in this paper, we propose a heterogeneous-attributes enhancement deep framework (HEDF), which could better capture the non-linear structure and associated information in a deep learning way, and effectively combine the structure information of multi-views by the combining layer. Along this line, the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode. The extensive validations on several real-world datasets show that our model could outperform the baselines, especially for the sparse and inconsistent situation with less training data.
Keyword:
network embedding
heterogeneous-attributes
deep framework
inconsistent
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.6
论文数:
1.6K
被引数:
2.8K
机构
引用论文
Insurance activity and economic performance: Fresh evidence from asymmetric panel causality tests
OAAI
OAAI分享收藏Passive signal propagation and membrane properties in median photoreceptors of the giant barnacleOAAI
OAAI分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏A Review of Technical Impact of Electrical Vehicle Charging Stations on Distribution Grid电动汽车充电站对配电网的技术影响研究综述
PREAI
分享收藏Aboveground Biomass Invariance Masks Significant Belowground Productivity Changes in Response to Salinization and Nitrogen Loading in Reed MarshesWetlands
WetlandsIF0
PREAI
分享收藏
查看更多内容