Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs
Knowledge graph embedding has been identified as an effective method for node-level classification tasks in directed graphs, the objective of which is to ensure that nodes of different categories are embedded as far apart as possible in the feature space. The directed graph is a general representati...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/12/23/3689 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850107053209550848 |
|---|---|
| author | Guoqiang Hou Qiwen Yu Fan Chen Guang Chen |
| author_facet | Guoqiang Hou Qiwen Yu Fan Chen Guang Chen |
| author_sort | Guoqiang Hou |
| collection | DOAJ |
| description | Knowledge graph embedding has been identified as an effective method for node-level classification tasks in directed graphs, the objective of which is to ensure that nodes of different categories are embedded as far apart as possible in the feature space. The directed graph is a general representation of unstructured knowledge graphs. However, existing methods lack the ability to simultaneously approximate high-order filters and globally pay attention to the task-related connectivity between distant nodes for directed graphs. To address this limitation, a directed spectral graph transformer (DSGT), a hybrid architecture model, is constructed by integrating the graph transformer and directed spectral graph convolution networks. The graph transformer leverages multi-head attention mechanisms to capture the global connectivity of the feature graph from different perspectives in the spatial domain, which bridges the gap between frequency responses and, further, naturally couples the graph transformer and directed graph convolutional neural networks (GCNs). In addition to the inherent hard inductive bias of DSGT, we introduce directed node positional and structure-aware edge embedding to provide topological prior knowledge. Extensive experiments demonstrate that the DSGT exhibits state-of-the-art (SOTA) or competitive node-level representation capabilities across datasets of varying attributes and scales. Furthermore, the experimental results indicate that the homophily and degree of correlation of the nodes significantly influence the classification performance of the model. This finding opens significant avenues for future research. |
| format | Article |
| id | doaj-art-98a01b4c5fa54caeb1f9c95f54adb71b |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-98a01b4c5fa54caeb1f9c95f54adb71b2025-08-20T02:38:41ZengMDPI AGMathematics2227-73902024-11-011223368910.3390/math12233689Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNsGuoqiang Hou0Qiwen Yu1Fan Chen2Guang Chen3College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Manufacturing, Chongqing Vocational and Technical College of Industry and Trade, Chongqing 401120, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaKnowledge graph embedding has been identified as an effective method for node-level classification tasks in directed graphs, the objective of which is to ensure that nodes of different categories are embedded as far apart as possible in the feature space. The directed graph is a general representation of unstructured knowledge graphs. However, existing methods lack the ability to simultaneously approximate high-order filters and globally pay attention to the task-related connectivity between distant nodes for directed graphs. To address this limitation, a directed spectral graph transformer (DSGT), a hybrid architecture model, is constructed by integrating the graph transformer and directed spectral graph convolution networks. The graph transformer leverages multi-head attention mechanisms to capture the global connectivity of the feature graph from different perspectives in the spatial domain, which bridges the gap between frequency responses and, further, naturally couples the graph transformer and directed graph convolutional neural networks (GCNs). In addition to the inherent hard inductive bias of DSGT, we introduce directed node positional and structure-aware edge embedding to provide topological prior knowledge. Extensive experiments demonstrate that the DSGT exhibits state-of-the-art (SOTA) or competitive node-level representation capabilities across datasets of varying attributes and scales. Furthermore, the experimental results indicate that the homophily and degree of correlation of the nodes significantly influence the classification performance of the model. This finding opens significant avenues for future research.https://www.mdpi.com/2227-7390/12/23/3689knowledge graph embeddingshybrid architecturegraph transformerdirected spectral graph convolution networksnode-level representation learning |
| spellingShingle | Guoqiang Hou Qiwen Yu Fan Chen Guang Chen Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs Mathematics knowledge graph embeddings hybrid architecture graph transformer directed spectral graph convolution networks node-level representation learning |
| title | Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs |
| title_full | Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs |
| title_fullStr | Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs |
| title_full_unstemmed | Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs |
| title_short | Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs |
| title_sort | directed knowledge graph embedding using a hybrid architecture of spatial and spectral gnns |
| topic | knowledge graph embeddings hybrid architecture graph transformer directed spectral graph convolution networks node-level representation learning |
| url | https://www.mdpi.com/2227-7390/12/23/3689 |
| work_keys_str_mv | AT guoqianghou directedknowledgegraphembeddingusingahybridarchitectureofspatialandspectralgnns AT qiwenyu directedknowledgegraphembeddingusingahybridarchitectureofspatialandspectralgnns AT fanchen directedknowledgegraphembeddingusingahybridarchitectureofspatialandspectralgnns AT guangchen directedknowledgegraphembeddingusingahybridarchitectureofspatialandspectralgnns |