图 书 馆
用于链接预测的学习谱图变换Learning Spectral Graph Transformations for Link Prediction |
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| 课程网址: | http://videolectures.net/icml09_kunegis_lsgtlp/ |
| 主讲教师: | Jérôme Kunegis |
| 开课单位: | 科布伦茨 - 兰道大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present a unified framework for learning link prediction and edge weight prediction functions in large networks, based on the transformation of a graph’s algebraic spectrum. Our approach generalizes several graph kernels and dimensionality reduction methods and provides a method to estimate their parameters efficiently. We show how the parameters of these prediction functions can be learned by reducing the problem to a one-dimensional regression problem whose runtime only depends on the method’s reduced rank and that can be inspected visually. We derive variants that apply to undirected, weighted, unweighted, unipartite and bipartite graphs. We evaluate our method experimentally using examples from social networks, collaborative filtering, trust networks, citation networks, authorship graphs and hyperlink networks. |
| 关 键 词: | 链路预测; 边缘权重预测; 一维回归问题 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-23：lxf |
| 阅读次数: | 79 |