Meta-path convolution based heterogeneous graph neural network algorithm
In the multilayer graph convolution calculation, each node is usually represented as a single vector, which makes the high-order graph convolution layer unable to distinguish the information of different relationships and sequences, resulting in the loss of information in the transmission process. T...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-03-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024078/ |
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| author | QIN Zhilong DENG Kun LIU Xingyan |
| author_facet | QIN Zhilong DENG Kun LIU Xingyan |
| author_sort | QIN Zhilong |
| collection | DOAJ |
| description | In the multilayer graph convolution calculation, each node is usually represented as a single vector, which makes the high-order graph convolution layer unable to distinguish the information of different relationships and sequences, resulting in the loss of information in the transmission process. To solve this problem, a heterogeneous graph neural network algorithm based on meta-path convolution was proposed. Firstly, the feature transformation was used to adaptively adjust the node features. Secondly, the high-order indirect relationship between the nodes was mined by convolution within the meta-path to capture the interaction between the target node and other types of nodes under the element path. Finally, the reciprocity between semantics was explored through the self-attention mechanism, and the features from different meta-paths were fused. Extensive experiments were carried out on ACM, IMDB and DBLP datasets, and compared with the current mainstream algorithms. The experimental results show that the average increase of Macro-F1 in the node classification task is 0.5%～3.5%, and the ARI value in the node clustering task is increased by 1%～3%, which proves that the algorithm is effective and feasible. |
| format | Article |
| id | doaj-art-f0b2261a4632465daf950683d7a85bab |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-03-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-f0b2261a4632465daf950683d7a85bab2025-01-15T02:48:22ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-03-01408910355075427Meta-path convolution based heterogeneous graph neural network algorithmQIN ZhilongDENG KunLIU XingyanIn the multilayer graph convolution calculation, each node is usually represented as a single vector, which makes the high-order graph convolution layer unable to distinguish the information of different relationships and sequences, resulting in the loss of information in the transmission process. To solve this problem, a heterogeneous graph neural network algorithm based on meta-path convolution was proposed. Firstly, the feature transformation was used to adaptively adjust the node features. Secondly, the high-order indirect relationship between the nodes was mined by convolution within the meta-path to capture the interaction between the target node and other types of nodes under the element path. Finally, the reciprocity between semantics was explored through the self-attention mechanism, and the features from different meta-paths were fused. Extensive experiments were carried out on ACM, IMDB and DBLP datasets, and compared with the current mainstream algorithms. The experimental results show that the average increase of Macro-F1 in the node classification task is 0.5%～3.5%, and the ARI value in the node clustering task is increased by 1%～3%, which proves that the algorithm is effective and feasible.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024078/heterogeneous graphgraph embeddinggraph neural networkmeta-pathgraph convolution |
| spellingShingle | QIN Zhilong DENG Kun LIU Xingyan Meta-path convolution based heterogeneous graph neural network algorithm Dianxin kexue heterogeneous graph graph embedding graph neural network meta-path graph convolution |
| title | Meta-path convolution based heterogeneous graph neural network algorithm |
| title_full | Meta-path convolution based heterogeneous graph neural network algorithm |
| title_fullStr | Meta-path convolution based heterogeneous graph neural network algorithm |
| title_full_unstemmed | Meta-path convolution based heterogeneous graph neural network algorithm |
| title_short | Meta-path convolution based heterogeneous graph neural network algorithm |
| title_sort | meta path convolution based heterogeneous graph neural network algorithm |
| topic | heterogeneous graph graph embedding graph neural network meta-path graph convolution |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024078/ |
| work_keys_str_mv | AT qinzhilong metapathconvolutionbasedheterogeneousgraphneuralnetworkalgorithm AT dengkun metapathconvolutionbasedheterogeneousgraphneuralnetworkalgorithm AT liuxingyan metapathconvolutionbasedheterogeneousgraphneuralnetworkalgorithm |