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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Chris Lin",
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  "author": "Chris Lin",
            
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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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  "doi": "10.57702/4nz3lndm",
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  "doi": "10.57702/4nz3lndm",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-03",
            
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  "doi_publisher": "TIB",
            
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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": "Gerald J. Sun",
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      "extra_author": "Gerald J. Sun",
            
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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": "Krishna C. Bulusu",
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      "extra_author": "Krishna C. Bulusu",
            
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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": "Jonathan R. Dry",
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      "extra_author": "Jonathan R. Dry",
            
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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": "Marylens Hernandez",
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      "extra_author": "Marylens Hernandez",
            
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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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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Graph Neural Networks",
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      "display_name": "Graph Neural Networks",
            
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      "name": "graph-neural-networks",
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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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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Interpretability",
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      "display_name": "Interpretability",
            
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      "name": "interpretability",
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      "name": "interpretability",
            
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      "title": "Interpretability"
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      "title": "Interpretability"
            
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    }
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    }
            
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  ],
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  ],
            
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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": "2024-12-03T09:52:47.941918",
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  "metadata_created": "2024-12-03T09:52:47.941918",
            
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  "metadata_modified": "2024-12-03T09:52:47.941924",
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  "metadata_modified": "2024-12-03T09:52:48.387534",
            
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  "name": 
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  "name": 
            
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"graph-neural-networks-including-sparse-interpretability--gisst-",
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"graph-neural-networks-including-sparse-interpretability--gisst-",
            
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  "notes": "Graph Neural Networks (GNNs) are versatile, powerful 
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  "notes": "Graph Neural Networks (GNNs) are versatile, powerful 
            
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machine learning methods that enable graph structure and feature 
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machine learning methods that enable graph structure and feature 
            
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representation learning, and have applications across many domains. 
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representation learning, and have applications across many domains. 
            
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For applications critically requiring interpretation, attention-based 
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For applications critically requiring interpretation, attention-based 
            
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GNNs have been leveraged. However, these approaches either rely on 
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GNNs have been leveraged. However, these approaches either rely on 
            
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specific model architectures or lack a joint consideration of graph 
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specific model architectures or lack a joint consideration of graph 
            
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structure and node features in their interpretation. Here we present a 
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structure and node features in their interpretation. Here we present a 
            
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model-agnostic framework for interpreting important graph structure 
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model-agnostic framework for interpreting important graph structure 
            
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and node features, Graph neural networks Including SparSe 
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and node features, Graph neural networks Including SparSe 
            
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inTerpretability (GISST). With any GNN model, GISST combines an 
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inTerpretability (GISST). With any GNN model, GISST combines an 
            
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attention mechanism and sparsity regularization to yield an important 
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attention mechanism and sparsity regularization to yield an important 
            
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subgraph and node feature subset related to any graph-based task.",
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subgraph and node feature subset related to any graph-based task.",
            
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      "name": "Attention Mechanism",
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    {
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    {
            
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      "display_name": "Graph Neural Networks",
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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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    {
            
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      "display_name": "Interpretability",
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      "display_name": "Interpretability",
            
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      "id": "7b0ef563-8fe8-47c6-8b53-25700094382a",
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      "id": "7b0ef563-8fe8-47c6-8b53-25700094382a",
            
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      "name": "Interpretability",
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      "name": "Interpretability",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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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": "Sparsity Regularization",
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      "display_name": "Sparsity Regularization",
            
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      "id": "bc55e823-533f-409a-b6fc-14080883a6a8",
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      "id": "bc55e823-533f-409a-b6fc-14080883a6a8",
            
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      "name": "Sparsity Regularization",
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      "name": "Sparsity Regularization",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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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": "Graph Neural Networks Including SparSe inTerpretability 
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  "title": "Graph Neural Networks Including SparSe inTerpretability 
            
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(GISST)",
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(GISST)",
            
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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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}