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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Yaming Yang",
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  "author": "Yaming Yang",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [
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  "citation": [
            
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    "https://doi.org/10.1109/TNNLS.2022.3210370",
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    "https://doi.org/10.1109/TNNLS.2022.3210370",
            
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    "https://doi.org/10.48550/arXiv.2212.00024",
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    "https://doi.org/10.48550/arXiv.2212.00024",
            
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    "https://doi.org/10.1109/TKDE.2021.3101356",
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    "https://doi.org/10.1109/TKDE.2021.3101356",
            
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    "https://doi.org/10.3390/e24020276",
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    "https://doi.org/10.3390/e24020276",
            
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    "https://doi.org/10.48550/arXiv.2210.03288"
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    "https://doi.org/10.48550/arXiv.2210.03288"
            
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  ],
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "defined_in": "https://doi.org/10.48550/arXiv.2107.02658",
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  "defined_in": "https://doi.org/10.48550/arXiv.2107.02658",
            
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  "doi": "10.57702/u1zw77gg",
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  "doi": "10.57702/u1zw77gg",
            
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  "doi_date_published": "2024-11-25",
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  "doi_date_published": "2024-11-25",
            
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  "doi_publisher": "TIB",
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  "domain": "https://service.tib.eu/ldmservice",
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Ziyu Guan",
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      "extra_author": "Ziyu Guan",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Jianxin Li",
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      "extra_author": "Jianxin Li",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Wei Zhao",
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      "extra_author": "Wei Zhao",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Jiangtao Cui",
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      "extra_author": "Jiangtao Cui",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Quan Wang",
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      "extra_author": "Quan Wang",
            
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      "orcid": ""
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    }
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      "name": "heterogeneous-graphs",
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      "name": "heterogeneous-graphs",
            
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      "title": "Heterogeneous Graphs"
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      "title": "Heterogeneous Graphs"
            
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      "name": "heterogeneous-information-network",
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      "name": "heterogeneous-information-network",
            
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      "title": "Heterogeneous Information Network"
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      "title": "Heterogeneous Information Network"
            
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      "name": "link-prediction",
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      "name": "link-prediction",
            
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      "title": "Link Prediction"
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      "title": "Link Prediction"
            
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      "description": "",
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      "name": "node-clustering",
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      "name": "node-clustering",
            
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      "title": "Node Clustering"
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      "title": "Node Clustering"
            
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      "description": "",
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      "name": "scientific-paper-classification",
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      "name": "scientific-paper-classification",
            
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      "title": "Scientific Paper Classification"
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      "title": "Scientific Paper Classification"
            
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      "description": "",
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      "name": "semi-supervised-learning",
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      "name": "semi-supervised-learning",
            
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      "title": "Semi-supervised Learning"
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      "title": "Semi-supervised Learning"
            
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  "name": "acm",
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  "notes": "The dataset used in the paper is a heterogeneous graph, 
            
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which is a graph that contains multiple types of nodes and edges. The 
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which is a graph that contains multiple types of nodes and edges. The 
            
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dataset is used for semi-supervised graph learning with multi-level 
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}