图 书 馆
使用结构注意的图形分类Graph Classification using Structural Attention |
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| 课程网址: | http://videolectures.net/kdd2018_lee_graph_classification/ |
| 主讲教师: | John Boaz Lee |
| 开课单位: | 伍斯特理工学院 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph features) that help discriminate between graphs of different classes. When calculating such features, most existing approaches process the entire graph. In a graphletbased approach, for instance, the entire graph is processed to get the total count of different graphlets or subgraphs. In many real-world applications, however, graphs can be noisy with discriminative patterns confined to certain regions in the graph only. In this work, we study the problem of attention-based graph classification. The use of attention allows us to focus on small but informative parts of the graph, avoiding noise in the rest of the graph. We present a novel RNN model, called the Graph Attention Model (GAM), that processes only a portion of the graph by adaptively selecting a sequence of “informative” nodes. Experimental results on multiple real-world datasets show that the proposed method is competitive against various well-known methods in graph classification even though our method is limited to only a portion of the graph. |
| 关 键 词: | 实际应用; 图注意模型; 真实世界 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-05：chenjy |
| 最后编审: | 2023-03-05：chenjy |
| 阅读次数: | 23 |