.png){kind=icon}
返回
Attributed network representation learning via improved graph attention with robust negative sampling
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-020-01825-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Attributed network representation learning is to embed graphs in low dimensional vector space such that the embedded vectors follow the differences and similarities of the source graphs. To capture structural features and node attributes of attributed network, we propose a novel graph auto-encoder method which is stacked encoder-decoder layers based on graph attention with robust negative sampling. Here, minimize the negative log-likelihood, triplet distance, and weighted neighborhood attributes are proposed as the loss function. To alleviate the over-fitting on reconstruct graph structural features or node attributes, a trade off algorithm between reconstruction loss of node attributes and reconstruction loss of structural features is proposed. Furthermore, to alleviate the impact of random sampling, we propose additional constraints on negative sampling based on node degree. Experimental results on several benchmark datasets for transductive and inductive learning tasks show that the proposed model is competitive against well-known methods in node classification and link prediction.
Keyword:
Graph attention
Robust negative sampling
Weighted neighborhood attributes
Triplet loss
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:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
Conjugated heat transfer and temperature distributions in a gas turbine combustion liner under base-load operation基本负荷运行下燃气轮机燃烧衬里中的共轭传热和温度分布
PREAI
分享收藏
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications图嵌入的综合调查: 问题，技术和应用
OAAI
OAAI分享收藏
查看更多内容