图 书 馆
用于链路预测的 Weisfeiler-Lehman 神经机Weisfeiler-Lehman Neural Machine for Link Prediction |
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| 课程网址: | http://videolectures.net/kdd2017_zhang_link_prediction/ |
| 主讲教师: | 张慕涵 |
| 开课单位: | 圣路易斯华盛顿大学 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper, we propose a next-generation link prediction method, Weisfeiler-Lehman Neural Machine (WLNM), which learns topological features in the form of graph patterns that promote the formation of links. WLNM has unmatched advantages including higher performance than state-of-the-art methods and universal applicability over various kinds of networks. WLNM extracts an enclosing subgraph of each target link and encodes the subgraph as an adjacency matrix. The key novelty of the encoding comes from a fast hashing-based Weisfeiler-Lehman (WL) algorithm that labels the vertices according to their structural roles in the subgraph while preserving the subgraph's intrinsic directionality. After that, a neural network is trained on these adjacency matrices to learn a predictive model. Compared with traditional link prediction methods, WLNM does not assume a particular link formation mechanism (such as common neighbors), but learns this mechanism from the graph itself. We conduct comprehensive experiments to show that WLNM not only outperforms a great number of state-of-the-art link prediction methods, but also consistently performs well across networks with different characteristics. |
| 关 键 词: | 链路预测; 神经网络; WLNM |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-11-28：wujk |
| 最后编审: | 2023-11-28：wujk |
| 阅读次数: | 27 |