OSGNN: Original graph and Subgraph aggregated Graph Neural Network
Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neig...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yan, Yeyu [verfasserIn] Li, Chao [verfasserIn] Yu, Yanwei [verfasserIn] Li, Xiangju [verfasserIn] Zhao, Zhongying [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 225 |
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| Übergeordnetes Werk: |
volume:225 |
| DOI / URN: |
10.1016/j.eswa.2023.120115 |
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| Katalog-ID: |
ELV009650911 |
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| 245 | 1 | 0 | |a OSGNN: Original graph and Subgraph aggregated Graph Neural Network |
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| 520 | |a Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN. | ||
| 650 | 4 | |a Graph neural networks | |
| 650 | 4 | |a Heterogeneous graphs | |
| 650 | 4 | |a Graph representation learning | |
| 650 | 4 | |a Heterogeneous networks | |
| 700 | 1 | |a Li, Chao |e verfasserin |0 (orcid)0000-0002-3131-2723 |4 aut | |
| 700 | 1 | |a Yu, Yanwei |e verfasserin |0 (orcid)0000-0002-5924-1410 |4 aut | |
| 700 | 1 | |a Li, Xiangju |e verfasserin |0 (orcid)0000-0002-2752-8222 |4 aut | |
| 700 | 1 | |a Zhao, Zhongying |e verfasserin |0 (orcid)0000-0002-5880-0225 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Expert systems with applications |d Amsterdam [u.a.] : Elsevier Science, 1990 |g 225 |h Online-Ressource |w (DE-627)320577961 |w (DE-600)2017237-0 |w (DE-576)11481807X |7 nnns |
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10.1016/j.eswa.2023.120115 doi (DE-627)ELV009650911 (ELSEVIER)S0957-4174(23)00617-6 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yan, Yeyu verfasserin (orcid)0000-0002-6288-453X aut OSGNN: Original graph and Subgraph aggregated Graph Neural Network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN. Graph neural networks Heterogeneous graphs Graph representation learning Heterogeneous networks Li, Chao verfasserin (orcid)0000-0002-3131-2723 aut Yu, Yanwei verfasserin (orcid)0000-0002-5924-1410 aut Li, Xiangju verfasserin (orcid)0000-0002-2752-8222 aut Zhao, Zhongying verfasserin (orcid)0000-0002-5880-0225 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 225 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:225 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 225 |
| spelling |
10.1016/j.eswa.2023.120115 doi (DE-627)ELV009650911 (ELSEVIER)S0957-4174(23)00617-6 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yan, Yeyu verfasserin (orcid)0000-0002-6288-453X aut OSGNN: Original graph and Subgraph aggregated Graph Neural Network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN. Graph neural networks Heterogeneous graphs Graph representation learning Heterogeneous networks Li, Chao verfasserin (orcid)0000-0002-3131-2723 aut Yu, Yanwei verfasserin (orcid)0000-0002-5924-1410 aut Li, Xiangju verfasserin (orcid)0000-0002-2752-8222 aut Zhao, Zhongying verfasserin (orcid)0000-0002-5880-0225 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 225 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:225 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 225 |
| allfields_unstemmed |
10.1016/j.eswa.2023.120115 doi (DE-627)ELV009650911 (ELSEVIER)S0957-4174(23)00617-6 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yan, Yeyu verfasserin (orcid)0000-0002-6288-453X aut OSGNN: Original graph and Subgraph aggregated Graph Neural Network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN. Graph neural networks Heterogeneous graphs Graph representation learning Heterogeneous networks Li, Chao verfasserin (orcid)0000-0002-3131-2723 aut Yu, Yanwei verfasserin (orcid)0000-0002-5924-1410 aut Li, Xiangju verfasserin (orcid)0000-0002-2752-8222 aut Zhao, Zhongying verfasserin (orcid)0000-0002-5880-0225 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 225 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:225 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 225 |
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10.1016/j.eswa.2023.120115 doi (DE-627)ELV009650911 (ELSEVIER)S0957-4174(23)00617-6 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yan, Yeyu verfasserin (orcid)0000-0002-6288-453X aut OSGNN: Original graph and Subgraph aggregated Graph Neural Network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN. Graph neural networks Heterogeneous graphs Graph representation learning Heterogeneous networks Li, Chao verfasserin (orcid)0000-0002-3131-2723 aut Yu, Yanwei verfasserin (orcid)0000-0002-5924-1410 aut Li, Xiangju verfasserin (orcid)0000-0002-2752-8222 aut Zhao, Zhongying verfasserin (orcid)0000-0002-5880-0225 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 225 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:225 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 225 |
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10.1016/j.eswa.2023.120115 doi (DE-627)ELV009650911 (ELSEVIER)S0957-4174(23)00617-6 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yan, Yeyu verfasserin (orcid)0000-0002-6288-453X aut OSGNN: Original graph and Subgraph aggregated Graph Neural Network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN. Graph neural networks Heterogeneous graphs Graph representation learning Heterogeneous networks Li, Chao verfasserin (orcid)0000-0002-3131-2723 aut Yu, Yanwei verfasserin (orcid)0000-0002-5924-1410 aut Li, Xiangju verfasserin (orcid)0000-0002-2752-8222 aut Zhao, Zhongying verfasserin (orcid)0000-0002-5880-0225 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 225 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:225 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 225 |
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Yan, Yeyu @@aut@@ Li, Chao @@aut@@ Yu, Yanwei @@aut@@ Li, Xiangju @@aut@@ Zhao, Zhongying @@aut@@ |
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Yan, Yeyu ddc 004 bkl 54.72 misc Graph neural networks misc Heterogeneous graphs misc Graph representation learning misc Heterogeneous networks OSGNN: Original graph and Subgraph aggregated Graph Neural Network |
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OSGNN: Original graph and Subgraph aggregated Graph Neural Network |
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OSGNN: Original graph and Subgraph aggregated Graph Neural Network |
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10.1016/j.eswa.2023.120115 |
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osgnn: original graph and subgraph aggregated graph neural network |
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OSGNN: Original graph and Subgraph aggregated Graph Neural Network |
| abstract |
Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN. |
| abstractGer |
Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN. |
| abstract_unstemmed |
Heterogeneous Graph Embedding (HGE) is receiving a great attention from researchers, as it can be widely and effectively used to solve problems from various real-world applications. The existing HGE models mainly learn node representation directly on the whole heterogeneous graph by aggregating neighboring information, which unavoidably leads to the loss of useful high-order information. Another mainstream is to split heterogeneous graphs into different homogeneous subgraphs and then learn representations separately. However, this isolated handling way is prone to the loss of important interactions between the nodes of the same type. To address the above challenging but interesting problems, we propose an Original graph and Subgraph aggregated Graph Neural Network (OSGNN). Specifically, we first split the original heterogeneous graph into several subgraphs, and then weighted combine them to get a new meaningful homogeneous graph. Finally, the first-order and high-order information of the target node are learned from the original heterogeneous graph and the homogeneous subgraph respectively and concatenated as the final node representation. Extensive experiments on three real-world heterogeneous graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods. The source codes of this work are available on https://github.com/ZZY-GraphMiningLab/OSGNN. |
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OSGNN: Original graph and Subgraph aggregated Graph Neural Network |
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|
| score |
7.400483 |