{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/edge2node-reducing-edge-prediction-to-node","title":"Edge2Node: Reducing Edge Prediction to Node Classification","arxiv_id":"2311.02921","date":"2023-11-06","proceeding":null,"authors":["Zahed Rahmati"],"abstract":"Despite the success of graph neural network models in node classification, edge prediction (the task of predicting missing or potential links between nodes in a graph) remains a challenging problem for these models. A common approach for edge prediction is to first obtain the embeddings of two nodes, and then a predefined scoring function is used to predict the existence of an edge between the two nodes. Here, we introduce a preliminary idea called Edge2Node which suggests to directly obtain an embedding for each edge, without the need for a scoring function. This idea wants to create a new graph H based on the graph G given for the edge prediction task, and then suggests reducing the edge prediction task on G to a node classification task on H. We anticipate that this introductory method could stimulate further investigations for edge prediction task.","url_abs":"https://arxiv.org/abs/2311.02921v3","url_pdf":"https://arxiv.org/pdf/2311.02921v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"link-property-prediction","task_name":"Link Property Prediction"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-ogbl-collab","task":"Link Prediction","dataset":"ogbl-collab","model":"Edge2Node","rank_in_archive_order":1,"of":1,"metrics":{"Test Hits@50":"0.9515"},"uses_additional_data":false},{"leaderboard":"/sota/link-property-prediction-on-ogbl-collab","task":"Link Property Prediction","dataset":"ogbl-collab","model":"E2N","rank_in_archive_order":1,"of":34,"metrics":{"Ext. data":"No","Number of params":"526851","Test Hits@50":"0.9515 ± 0.1410","Validation Hits@50":"0.9546 ± 0.1270"},"uses_additional_data":false},{"leaderboard":"/sota/link-property-prediction-on-ogbl-ppa","task":"Link Property Prediction","dataset":"ogbl-ppa","model":"** E2N**","rank_in_archive_order":1,"of":26,"metrics":{"Ext. data":"No","Number of params":"526851","Test Hits@100":"0.8911 ± 0.1266","Validation Hits@100":"0.8857 ± 0.1331"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}