图 书 馆
图在双曲空间中的神经嵌入Neural Embeddings of Graphs in Hyperbolic Space |
|
| 课程网址: | http://videolectures.net/kdd2017_chamberlain_neural_embeddings/ |
| 主讲教师: | Ben Chamberlain |
| 开课单位: | 伦敦帝国理工学院 |
| 开课时间: | 2017-12-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Neural embeddings have been used with great success in Natural Language Processing (NLP). They provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted significant amounts of research into applications in domains other than language. One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling. For both NLP and graph based tasks, embeddings have been learned in high-dimensional Euclidean spaces. However, recent work has shown that the appropriate isometric space for embedding complex networks is not the flat Euclidean space, but negatively curved, hyperbolic space. We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space. We provide experimental evidence that embedding graphs in their natural geometry significantly improves performance on downstream tasks for several real-world public datasets. |
| 关 键 词: | 双曲空间; 图形嵌入; 神经嵌入 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-06-19：chenxin01 |
| 最后编审: | 2023-06-19：chenxin01 |
| 阅读次数: | 23 |