图 书 馆
基于局部结构相似性的网络内分类Within-Network Classification Using Local Structure Similarity |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_desrosiers_wncu/ |
| 主讲教师: | Christian Desrosiers |
| 开课单位: | 蒙特利尔综合理工学院 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Within-network classification, where the goal is to classify the nodes of a partly labeled network, is a semi-supervised learning problem that has applications in several important domains like image processing, the classification of documents, and the detection of malicious activities. While most methods for this problem infer the missing labels collectively based on the hypothesis that linked or nearby nodes are likely to have the same labels, there are many types of networks for which this assumption fails, e.g., molecular graphs, trading networks, etc. In this paper, we present a collective classification method, based on relaxation labeling, that classifies entities of a network using their local structure. This method uses a marginalized similarity kernel that compares the local structure of two nodes with random walks in the network. Through experimentation on different datasets, we show our method to be more accurate than several state-of-the-art approaches for this problem. |
| 关 键 词: | 网络节点; 半监督学习; 缺失标签 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-24：cwx |
| 阅读次数: | 65 |