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  "author": "Shirui Pan",
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  "author": "Shirui Pan",
            
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  "author_email": "",
            
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  "defined_in": "https://doi.org/10.48550/arXiv.1802.04407",
            
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  "doi": "10.57702/1svmnj1p",
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  "doi": "10.57702/1svmnj1p",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-16",
            
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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": "Ruiqi Hu",
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      "extra_author": "Ruiqi Hu",
            
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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": "Guodong Long",
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      "extra_author": "Guodong Long",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    {
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      "extra_author": "Jing Jiang",
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      "extra_author": "Jing Jiang",
            
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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": "Lina Yao",
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      "extra_author": "Lina Yao",
            
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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": "Chengqi Zhang",
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      "extra_author": "Chengqi Zhang",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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      "description": "",
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      "description": "",
            
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      "display_name": "Graph Embedding",
            
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      "name": "graph-embedding",
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      "name": "graph-embedding",
            
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      "title": "Graph Embedding"
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      "title": "Graph Embedding"
            
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    }
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  ],
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  "metadata_modified": "2024-12-16T18:13:56.134686",
            
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  "name": 
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  "name": 
            
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"adversarially-regularized-graph-autoencoder-for-graph-embedding",
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"adversarially-regularized-graph-autoencoder-for-graph-embedding",
            
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  "notes": "Graph embedding is an effective method to represent graph 
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  "notes": "Graph embedding is an effective method to represent graph 
            
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data in a low dimensional space for graph analytics. Most existing 
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data in a low dimensional space for graph analytics. Most existing 
            
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embedding algorithms typically focus on preserving the topological 
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embedding algorithms typically focus on preserving the topological 
            
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structure or minimizing the reconstruction errors of graph data, but 
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structure or minimizing the reconstruction errors of graph data, but 
            
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they have mostly ignored the data distribution of the latent codes 
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they have mostly ignored the data distribution of the latent codes 
            
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from the graphs, which often results in inferior embedding in 
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from the graphs, which often results in inferior embedding in 
            
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real-world graph data.",
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real-world graph data.",
            
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      "name": "Graph Autoencoder",
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      "name": "Graph Embedding",
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  "title": "Adversarially Regularized Graph Autoencoder for Graph 
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  "title": "Adversarially Regularized Graph Autoencoder for Graph 
            
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Embedding",
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Embedding",
            
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}