{"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/topology-adaptive-graph-convolutional","title":"Topology Adaptive Graph Convolutional Networks","arxiv_id":"1710.10370","date":"2017-10-28","proceeding":"ICLR 2018 1","authors":["Jian Du","Shanghang Zhang","Guanhang Wu","Jose M. F.  Moura","Soummya Kar"],"abstract":"Spectral graph convolutional neural networks (CNNs) require approximation to\nthe convolution to alleviate the computational complexity, resulting in\nperformance loss. This paper proposes the topology adaptive graph convolutional\nnetwork (TAGCN), a novel graph convolutional network defined in the vertex\ndomain. We provide a systematic way to design a set of fixed-size learnable\nfilters to perform convolutions on graphs. The topologies of these filters are\nadaptive to the topology of the graph when they scan the graph to perform\nconvolution. The TAGCN not only inherits the properties of convolutions in CNN\nfor grid-structured data, but it is also consistent with convolution as defined\nin graph signal processing. Since no approximation to the convolution is\nneeded, TAGCN exhibits better performance than existing spectral CNNs on a\nnumber of data sets and is also computationally simpler than other recent\nmethods.","url_abs":"http://arxiv.org/abs/1710.10370v5","url_pdf":"http://arxiv.org/pdf/1710.10370v5.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":[{"paper_slug":"topology-adaptive-graph-convolutional","repo_url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/tagcn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"topology-adaptive-graph-convolutional","repo_url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/tagcn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1710.10370","atlas_url":"https://app.syntology.ai/?focus=1710.10370","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}