{"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/dgcnn-disordered-graph-convolutional-neural","title":"DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model","arxiv_id":"1712.03563","date":"2017-12-10","proceeding":null,"authors":["Bo Wu","Yang Liu","Bo Lang","Lei Huang"],"abstract":"Convolutional neural networks (CNNs) can be applied to graph similarity\nmatching, in which case they are called graph CNNs. Graph CNNs are attracting\nincreasing attention due to their effectiveness and efficiency. However, the\nexisting convolution approaches focus only on regular data forms and require\nthe transfer of the graph or key node neighborhoods of the graph into the same\nfixed form. During this transfer process, structural information of the graph\ncan be lost, and some redundant information can be incorporated. To overcome\nthis problem, we propose the disordered graph convolutional neural network\n(DGCNN) based on the mixed Gaussian model, which extends the CNN by adding a\npreprocessing layer called the disordered graph convolutional layer (DGCL). The\nDGCL uses a mixed Gaussian function to realize the mapping between the\nconvolution kernel and the nodes in the neighborhood of the graph. The output\nof the DGCL is the input of the CNN. We further implement a\nbackward-propagation optimization process of the convolutional layer by which\nwe incorporate the feature-learning model of the irregular node neighborhood\nstructure into the network. Thereafter, the optimization of the convolution\nkernel becomes part of the neural network learning process. The DGCNN can\naccept arbitrary scaled and disordered neighborhood graph structures as the\nreceptive fields of CNNs, which reduces information loss during graph\ntransformation. Finally, we perform experiments on multiple standard graph\ndatasets. The results show that the proposed method outperforms the\nstate-of-the-art methods in graph classification and retrieval.","url_abs":"http://arxiv.org/abs/1712.03563v1","url_pdf":"http://arxiv.org/pdf/1712.03563v1.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-similarity","task_name":"Graph Similarity"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-classification-on-aids","task":"Graph Classification","dataset":"AIDS","model":"DGCNN","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy":"65.1"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-collab","task":"Graph Classification","dataset":"COLLAB","model":"DGCNN","rank_in_archive_order":35,"of":39,"metrics":{"Accuracy":"68.34%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-dd","task":"Graph Classification","dataset":"D&D","model":"DGCNN","rank_in_archive_order":34,"of":53,"metrics":{"Accuracy":"77.21%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-proteins","task":"Graph Classification","dataset":"PROTEINS","model":"DGCNN","rank_in_archive_order":70,"of":103,"metrics":{"Accuracy":"75.1%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-ptc","task":"Graph Classification","dataset":"PTC","model":"DGCNN","rank_in_archive_order":22,"of":37,"metrics":{"Accuracy":"65.43%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.03563","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}