Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning
文献类型：期刊论文
| 作者 | Zhang, Xun1; Yang, Lanyan1; Zhang, Bin1; Liu, Ying1; Jiang, Dong2 ; Qin, Xiaohai1; Hao, Mengmeng2
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| 刊名 | ENTROPY
 |
| 出版日期 | 2021-04-01 |
| 卷号 | 23期号:4页码:17 |
| 关键词 | graph analysis graph neural network semi-supervised learning neighborhood aggregation |
| DOI | 10.3390/e23040403 |
| 通讯作者 | Liu, Ying(liu_ying@th.btbu.edu.cn) ; Jiang, Dong(jiangd@igsnrr.ac.cn) |
| 英文摘要 | The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures. |
| 资助项目 | National Key Research and Development Program of China[2020YFB1806500] ; Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan[CITTCD201904037] ; R&D Program of Beijing Municipal Education Commission[KM202010011012] |
| WOS研究方向 | Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000642997100001 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China ; Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan ; R&D Program of Beijing Municipal Education Commission |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/161608]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Ying; Jiang, Dong |
| 作者单位 | 1.Beijing Technol & Business Univ, Sch Comp Sci & Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Resources Utilizat & Environm Remediat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Xun,Yang, Lanyan,Zhang, Bin,et al. Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning[J]. ENTROPY,2021,23(4):17. |
| APA | Zhang, Xun.,Yang, Lanyan.,Zhang, Bin.,Liu, Ying.,Jiang, Dong.,...&Hao, Mengmeng.(2021).Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning.ENTROPY,23(4),17. |
| MLA | Zhang, Xun,et al."Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning".ENTROPY 23.4(2021):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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