图 书 馆
用于链路预测的谱图变换学习方法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: |  我们提供了一个统一的框架，用于基于图的代数谱的转换来学习大型网络中的链接预测和边缘权重预测功能。我们的方法概括了几种图形核和降维方法，并提供了一种有效估计其参数的方法。我们展示了如何通过将问题简化为一维回归问题来学习这些预测函数的参数，该问题的运行时间仅取决于方法的降级，并且可以直观地进行检查。我们得出适用于无向图，加权图，未加权图，单方图和二分图的变体。我们使用来自社交网络，协作过滤，信任网络，引用网络，作者图和超链接网络的示例通过实验对我们的方法进行评估。 |
| 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: | 代数谱; 函数; 网络 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-02-16：nkq |
| 最后编审: | 2021-02-16：nkq |
| 阅读次数: | 47 |