图 书 馆
通过矩阵分解的链路预测Link prediction via matrix factorization |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_elkan_factorization/ |
| 主讲教师: | Charles Elkan |
| 开课单位: | 加州大学圣地亚哥分校 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose to solve the link prediction problem in graphs using a supervised matrix factorization approach. The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores. We show how these latent features may be combined with optional explicit features for nodes or edges, which yields better performance than using either type of feature exclusively. Finally, we propose a novel approach to address the class imbalance problem which is common in link prediction by directly optimizing for a ranking loss. Our model is optimized with stochastic gradient descent and scales to large graphs. Results on several datasets show the efficacy of our approach. |
| 关 键 词: | 网络分析; 计算机科学; 机器学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-02-03：nkq |
| 阅读次数: | 74 |