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  "access_rights": "",
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  "author": "Ao Zhang",
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  "author": "Ao Zhang",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [],
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  "defined_in": "https://doi.org/10.48550/arXiv.2006.08900",
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  "defined_in": "https://doi.org/10.48550/arXiv.2006.08900",
            
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  "doi": "10.57702/lucrc0b4",
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  "doi": "10.57702/lucrc0b4",
            
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  "doi_date_published": null,
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  "doi_date_published": "2025-01-03",
            
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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": "Jinwen Ma",
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      "extra_author": "Jinwen Ma",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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    {
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      "description": "",
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      "description": "",
            
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      "display_name": "Adversarial Attacks",
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      "name": "adversarial-attacks",
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      "name": "adversarial-attacks",
            
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      "title": "Adversarial Attacks"
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      "title": "Adversarial Attacks"
            
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    },
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    },
            
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "name": "defense",
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      "name": "defense",
            
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      "title": "Defense"
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      "title": "Defense"
            
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      "description": "",
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      "description": "",
            
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      "name": "graph-neural-networks",
            
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      "title": "Graph Neural Networks"
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      "title": "Graph Neural Networks"
            
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    }
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    }
            
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  ],
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  "landing_page": "https://github.com/zhangao520/defense-vgae",
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  "landing_page": "https://github.com/zhangao520/defense-vgae",
            
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  "license_title": null,
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  "license_title": null,
            
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  "link_orkg": "",
            
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  "metadata_created": "2025-01-03T00:36:11.788582",
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  "metadata_created": "2025-01-03T00:36:11.788582",
            
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  "metadata_modified": "2025-01-03T00:36:12.427917",
            
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  "name": 
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  "name": 
            
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dversarial-attacks-on-graph-data-via-a-variational-graph-autoencoder",
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dversarial-attacks-on-graph-data-via-a-variational-graph-autoencoder",
            
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  "notes": "Graph neural networks (GNNs) achieve remarkable 
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  "notes": "Graph neural networks (GNNs) achieve remarkable 
            
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performance for tasks on graph data. However, recent works show they 
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performance for tasks on graph data. However, recent works show they 
            
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are extremely vulnerable to adversarial structural perturbations, 
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are extremely vulnerable to adversarial structural perturbations, 
            
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making their outcomes unreliable. In this paper, we propose 
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making their outcomes unreliable. In this paper, we propose 
            
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DefenseVGAE, a novel framework leveraging variational graph 
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DefenseVGAE, a novel framework leveraging variational graph 
            
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autoencoders (VGAEs) to defend GNNs against such attacks.",
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autoencoders (VGAEs) to defend GNNs against such attacks.",
            
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    "approval_status": "approved",
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      "display_name": "Adversarial Attacks",
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      "display_name": "Defense",
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      "display_name": "Defense",
            
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      "id": "74fd4012-367d-4c9c-995e-e16dcfcb08d6",
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      "id": "74fd4012-367d-4c9c-995e-e16dcfcb08d6",
            
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      "name": "Defense",
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      "name": "Defense",
            
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      "display_name": "Graph Neural Networks",
            
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      "id": "8c619694-1a66-4a7d-bcda-bb2d6265d082",
            
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      "name": "Graph Neural Networks",
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      "name": "Graph Neural Networks",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "Variational Graph Autoencoder",
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      "display_name": "Variational Graph Autoencoder",
            
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      "id": "fcf0df6d-c3b5-4508-b701-332db827cfb9",
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      "id": "fcf0df6d-c3b5-4508-b701-332db827cfb9",
            
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      "name": "Variational Graph Autoencoder",
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      "name": "Variational Graph Autoencoder",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "DefenseVGAE: Defending against Adversarial Attacks on 
152
  "title": "DefenseVGAE: Defending against Adversarial Attacks on 
            
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Graph Data via a Variational Graph Autoencoder",
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Graph Data via a Variational Graph Autoencoder",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}