{"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/censnet-convolution-with-edge-node-switching","title":"CensNet: Convolution with Edge-Node Switching in Graph Neural Networks","arxiv_id":null,"date":"2019-08-10","proceeding":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) 2019 8","authors":["Xiaodong Jiang","Pengsheng Ji","Sheng Li"],"abstract":"In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using line graph of the original undirected graph, the role of nodes and edges are switched, and two novel graph convolution operations are proposed for feature propagation. Experimental results on real-world academic citation networks and quantum chemistry graphs show that our approach has achieved or matched the state-of-the-art performance.","url_abs":"https://doi.org/10.24963/ijcai.2019/369","url_pdf":"https://www.ijcai.org/proceedings/2019/0369.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-classification","task_name":"Graph Classification"},{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"graph-regression","task_name":"Graph Regression"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-regression-on-lipophilicity","task":"Graph Regression","dataset":"Lipophilicity","model":"CensNet","rank_in_archive_order":21,"of":23,"metrics":{"RMSE@80%Train":"0.93"},"uses_additional_data":false},{"leaderboard":"/sota/graph-regression-on-lipophilicity","task":"Graph Regression","dataset":"Lipophilicity","model":"Logistic Regression","rank_in_archive_order":22,"of":23,"metrics":{"RMSE@80%Train":"1.15"},"uses_additional_data":false},{"leaderboard":"/sota/graph-regression-on-lipophilicity","task":"Graph Regression","dataset":"Lipophilicity","model":"Random Forests","rank_in_archive_order":23,"of":23,"metrics":{"RMSE@80%Train":"1.16"},"uses_additional_data":false},{"leaderboard":"/sota/graph-regression-on-tox21","task":"Graph Regression","dataset":"Tox21","model":"CensNet","rank_in_archive_order":1,"of":3,"metrics":{"AUC@80%Train":"0.78"},"uses_additional_data":false},{"leaderboard":"/sota/graph-regression-on-tox21","task":"Graph Regression","dataset":"Tox21","model":"Random Forest","rank_in_archive_order":2,"of":3,"metrics":{"AUC@80%Train":"0.71"},"uses_additional_data":false},{"leaderboard":"/sota/graph-regression-on-tox21","task":"Graph Regression","dataset":"Tox21","model":"Logistic Regression","rank_in_archive_order":3,"of":3,"metrics":{"AUC@80%Train":"0.71"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}