{"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/adaptive-sampling-towards-fast-graph","title":"Adaptive Sampling Towards Fast Graph Representation Learning","arxiv_id":"1809.05343","date":"2018-09-14","proceeding":"NeurIPS 2018 12","authors":["Wenbing Huang","Tong Zhang","Yu Rong","Junzhou Huang"],"abstract":"Graph Convolutional Networks (GCNs) have become a crucial tool on learning\nrepresentations of graph vertices. The main challenge of adapting GCNs on\nlarge-scale graphs is the scalability issue that it incurs heavy cost both in\ncomputation and memory due to the uncontrollable neighborhood expansion across\nlayers. In this paper, we accelerate the training of GCNs through developing an\nadaptive layer-wise sampling method. By constructing the network layer by layer\nin a top-down passway, we sample the lower layer conditioned on the top one,\nwhere the sampled neighborhoods are shared by different parent nodes and the\nover expansion is avoided owing to the fixed-size sampling. More importantly,\nthe proposed sampler is adaptive and applicable for explicit variance\nreduction, which in turn enhances the training of our method. Furthermore, we\npropose a novel and economical approach to promote the message passing over\ndistant nodes by applying skip connections. Intensive experiments on several\nbenchmarks verify the effectiveness of our method regarding the classification\naccuracy while enjoying faster convergence speed.","url_abs":"http://arxiv.org/abs/1809.05343v3","url_pdf":"http://arxiv.org/pdf/1809.05343v3.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":"adaptive-sampling-towards-fast-graph","repo_url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/_deprecated/adaptive_sampling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"adaptive-sampling-towards-fast-graph","repo_url":"https://github.com/huangwb/AS-GCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"graph-representation-learning","task_name":"Graph Representation Learning"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/node-classification-on-citeseer-full","task":"Node Classification","dataset":"Citeseer Full-supervised","model":"ASGCN","rank_in_archive_order":2,"of":7,"metrics":{"Accuracy":"79.66%"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-cora","task":"Node Classification","dataset":"Cora","model":"AS-GCN","rank_in_archive_order":16,"of":73,"metrics":{"Accuracy":"87.44% ± 0.0034%"},"uses_additional_data":true},{"leaderboard":"/sota/node-classification-on-cora-full-supervised","task":"Node Classification","dataset":"Cora Full-supervised","model":"ASGCN","rank_in_archive_order":4,"of":9,"metrics":{"Accuracy":"87.44±0.0034%"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-pubmed-full-supervised","task":"Node Classification","dataset":"Pubmed Full-supervised","model":"ASGCN","rank_in_archive_order":2,"of":7,"metrics":{"Accuracy":"90.6%"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-reddit","task":"Node Classification","dataset":"Reddit","model":"ASGCN","rank_in_archive_order":9,"of":16,"metrics":{"Accuracy":"96.27%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.05343","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}