Graph Embedding Method based on Space Syntax and Improved K-Means Clustering
发布时间：2024-08-09
点击次数：
- 所属单位：
- 信息与控制工程学院
- 发表刊物：
- Advanced Materials Research
- 关键字：
- 中文关键字：结构模式识别；图嵌入；空间句法；K均值；拓扑；统计模式识别，英文关键字：structural pattern recognition; graph embedding; s
- 摘要：
- The main drawbacks of structural pattern recognition compared to statistical pattern recognition are the high computation complexity and fewer processing tools that are available in the domain. To bridge the gap between the structural and statistical pattern recognition, a new graph embedding method based on space syntax and improved K-means clustering is proposed. The present paper uses the space syntax theory to build quantitative description of the nodes’ topological features, and then combines the proposed topological features with non-topological features in other aspects of the domain to construct node feature set using an improved K-means clustering algorithm, and then maps the graph into vector space explicitly by a statistical approach. Thus SVM can be applied to achieve graph classification. The experimental results show that such an embedding method can achieve higher classification accuracy in different graph datasets.
- 备注：
- 李智杰
- 合写作者：
- 刘欣
- 第一作者：
- 郑普亮,李昌华,李智杰
- 论文类型：
- 期刊论文
- 卷号：
- 卷：1044-1045
- 期号：
- 期：
- 页面范围：
- 页：1163-1168
- 是否译文：
- 否
- 发表时间：
- 2014-11-01
- 上一条：多尺度特征融合的图嵌入方法