Heterogeneous graph neural network for attribute completion
Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Kai [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea - Wang, Jiliang ELSEVIER, 2018, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:251 ; year:2022 ; day:5 ; month:09 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.knosys.2022.109171 |
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| 520 | |a Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. | ||
| 520 | |a Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. | ||
| 650 | 7 | |a Attribute completion |2 Elsevier | |
| 650 | 7 | |a Graph neural network |2 Elsevier | |
| 650 | 7 | |a Network embedding |2 Elsevier | |
| 650 | 7 | |a Heterogeneous networks |2 Elsevier | |
| 650 | 7 | |a Attributed networks |2 Elsevier | |
| 700 | 1 | |a Yu, Yanwei |4 oth | |
| 700 | 1 | |a Huang, Chao |4 oth | |
| 700 | 1 | |a Zhao, Zhongying |4 oth | |
| 700 | 1 | |a Dong, Junyu |4 oth | |
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10.1016/j.knosys.2022.109171 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001848.pica (DE-627)ELV058441506 (ELSEVIER)S0950-7051(22)00582-2 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Wang, Kai verfasserin aut Heterogeneous graph neural network for attribute completion 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. Attribute completion Elsevier Graph neural network Elsevier Network embedding Elsevier Heterogeneous networks Elsevier Attributed networks Elsevier Yu, Yanwei oth Huang, Chao oth Zhao, Zhongying oth Dong, Junyu oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:251 year:2022 day:5 month:09 pages:0 https://doi.org/10.1016/j.knosys.2022.109171 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 251 2022 5 0905 0 |
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10.1016/j.knosys.2022.109171 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001848.pica (DE-627)ELV058441506 (ELSEVIER)S0950-7051(22)00582-2 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Wang, Kai verfasserin aut Heterogeneous graph neural network for attribute completion 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. Attribute completion Elsevier Graph neural network Elsevier Network embedding Elsevier Heterogeneous networks Elsevier Attributed networks Elsevier Yu, Yanwei oth Huang, Chao oth Zhao, Zhongying oth Dong, Junyu oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:251 year:2022 day:5 month:09 pages:0 https://doi.org/10.1016/j.knosys.2022.109171 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 251 2022 5 0905 0 |
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10.1016/j.knosys.2022.109171 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001848.pica (DE-627)ELV058441506 (ELSEVIER)S0950-7051(22)00582-2 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Wang, Kai verfasserin aut Heterogeneous graph neural network for attribute completion 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. Attribute completion Elsevier Graph neural network Elsevier Network embedding Elsevier Heterogeneous networks Elsevier Attributed networks Elsevier Yu, Yanwei oth Huang, Chao oth Zhao, Zhongying oth Dong, Junyu oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:251 year:2022 day:5 month:09 pages:0 https://doi.org/10.1016/j.knosys.2022.109171 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 251 2022 5 0905 0 |
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10.1016/j.knosys.2022.109171 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001848.pica (DE-627)ELV058441506 (ELSEVIER)S0950-7051(22)00582-2 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Wang, Kai verfasserin aut Heterogeneous graph neural network for attribute completion 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. Attribute completion Elsevier Graph neural network Elsevier Network embedding Elsevier Heterogeneous networks Elsevier Attributed networks Elsevier Yu, Yanwei oth Huang, Chao oth Zhao, Zhongying oth Dong, Junyu oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:251 year:2022 day:5 month:09 pages:0 https://doi.org/10.1016/j.knosys.2022.109171 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 251 2022 5 0905 0 |
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10.1016/j.knosys.2022.109171 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001848.pica (DE-627)ELV058441506 (ELSEVIER)S0950-7051(22)00582-2 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Wang, Kai verfasserin aut Heterogeneous graph neural network for attribute completion 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. Attribute completion Elsevier Graph neural network Elsevier Network embedding Elsevier Heterogeneous networks Elsevier Attributed networks Elsevier Yu, Yanwei oth Huang, Chao oth Zhao, Zhongying oth Dong, Junyu oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:251 year:2022 day:5 month:09 pages:0 https://doi.org/10.1016/j.knosys.2022.109171 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 251 2022 5 0905 0 |
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Enthalten in Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea Amsterdam [u.a.] volume:251 year:2022 day:5 month:09 pages:0 |
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However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. 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Heterogeneous graph neural network for attribute completion |
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Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. |
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Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. |
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Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN. |
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Heterogeneous graph neural network for attribute completion |
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