{"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/advancing-graphsage-with-a-data-driven-node","title":"Advancing GraphSAGE with A Data-Driven Node Sampling","arxiv_id":"1904.12935","date":"2019-04-29","proceeding":null,"authors":["Jihun Oh","Kyunghyun Cho","Joan Bruna"],"abstract":"As an efficient and scalable graph neural network, GraphSAGE has enabled an\ninductive capability for inferring unseen nodes or graphs by aggregating\nsubsampled local neighborhoods and by learning in a mini-batch gradient descent\nfashion. The neighborhood sampling used in GraphSAGE is effective in order to\nimprove computing and memory efficiency when inferring a batch of target nodes\nwith diverse degrees in parallel. Despite this advantage, the default uniform\nsampling suffers from high variance in training and inference, leading to\nsub-optimum accuracy. We propose a new data-driven sampling approach to reason\nabout the real-valued importance of a neighborhood by a non-linear regressor,\nand to use the value as a criterion for subsampling neighborhoods. The\nregressor is learned using a value-based reinforcement learning. The implied\nimportance for each combination of vertex and neighborhood is inductively\nextracted from the negative classification loss output of GraphSAGE. As a\nresult, in an inductive node classification benchmark using three datasets, our\nmethod enhanced the baseline using the uniform sampling, outperforming recent\nvariants of a graph neural network in accuracy.","url_abs":"http://arxiv.org/abs/1904.12935v1","url_pdf":"http://arxiv.org/pdf/1904.12935v1.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":"advancing-graphsage-with-a-data-driven-node","repo_url":"https://github.com/oj9040/GraphSAGE_RL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"},{"method_slug":"graphsage","method_name":"GraphSAGE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.12935","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}