{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:51:30Z","timestamp":1783439490757,"version":"3.54.6"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"EU Horizon 2020","award":["101008297"],"award-info":[{"award-number":["101008297"]}]},{"name":"U.K. Research and Innovation"},{"name":"U.K. government’s Horizon Europe funding guarantee","award":["EP\/Y036786\/1"],"award-info":[{"award-number":["EP\/Y036786\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Parallel Distrib. Syst."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1109\/tpds.2025.3609152","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T17:33:31Z","timestamp":1757612011000},"page":"2354-2365","source":"Crossref","is-referenced-by-count":2,"title":["XDGNN: Efficient Distributed GNN Training via Explanation-Guided Subgraph Expansion"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3542-5389","authenticated-orcid":false,"given":"Jie","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5406-8420","authenticated-orcid":false,"given":"Jia","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-7314","authenticated-orcid":false,"given":"Geyong","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5288-5523","authenticated-orcid":false,"given":"Fei","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Shaanxi Normal University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"ref2","article-title":"How powerful are graph neural networks","author":"Xu","year":"2018"},{"key":"ref3","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"ref4","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463028"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26679"},{"key":"ref7","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"ref9","article-title":"GraphSAINT: Graph sampling based inductive learning method","author":"Zeng","year":"2019"},{"key":"ref10","first-page":"443","article-title":"NeuGraph: Parallel deep neural network computation on large graphs","volume-title":"Proc. 2019 USENIX Annu. Tech. Conf.","author":"Ma"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352127"},{"key":"ref12","first-page":"187","article-title":"Improving the accuracy, scalability, and performance of graph neural networks with ROC","volume-title":"Proc. Mach. Learn. Syst.","author":"Jia"},{"key":"ref13","first-page":"495","article-title":"Dorylus: Affordable, scalable, and accurate GNN training with distributed CPU servers and serverless threads","volume-title":"Proc. 15th USENIX Symp. Operating Syst. Des. Implementation","author":"Thorpe"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IA351965.2020.00011"},{"key":"ref15","first-page":"673","article-title":"BNS-GCN: Efficient full-graph training of graph convolutional networks with partition-parallelism and random boundary node sampling","volume-title":"Proc. Mach. Learn. Syst.","author":"Wan"},{"key":"ref16","article-title":"PipeGCN: Efficient full-graph training of graph convolutional networks with pipelined feature communication","author":"Wan","year":"2022"},{"key":"ref17","article-title":"Learn locally, correct globally: A distributed algorithm for training graph neural networks","author":"Ramezani","year":"2021"},{"key":"ref18","article-title":"Distributed training of graph convolutional networks using subgraph approximation","author":"Angerd","year":"2020"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3288755"},{"key":"ref20","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"Simonyan","year":"2013"},{"key":"ref21","first-page":"3319","article-title":"Axiomatic attribution for deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sundararajan"},{"key":"ref22","first-page":"9244","article-title":"GNNExplainer: Generating explanations for graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ying"},{"key":"ref23","first-page":"19620","article-title":"Parameterized explainer for graph neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Luo"},{"key":"ref24","article-title":"Interpreting graph neural networks for NLP with differentiable edge masking","author":"Schlichtkrull","year":"2020"},{"key":"ref25","article-title":"GraphFramEx: Towards systematic evaluation of explainability methods for graph neural networks","author":"Amara","year":"2022"},{"key":"ref26","article-title":"Explainability techniques for graph convolutional networks","author":"Baldassarre","year":"2019"},{"key":"ref27","article-title":"METIS: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices","author":"Karypis","year":"1997"},{"key":"ref28","article-title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks","author":"Wang","year":"2019"},{"key":"ref29","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hu"},{"key":"ref30","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3648358"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3065737"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2024.3371332"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00074"},{"key":"ref35","first-page":"551","article-title":"P3: Distributed deep graph learning at scale","volume-title":"Proc. 15th USENIX Symp. Operating Syst. Des. Implementation","author":"Gandhi"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2023.3295184"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref38","article-title":"FastGCN: Fast learning with graph convolutional networks via importance sampling","author":"Chen","year":"2018"},{"key":"ref39","first-page":"11249","article-title":"Layer-dependent importance sampling for training deep and large graph convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zou"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204236"}],"container-title":["IEEE Transactions on Parallel and Distributed Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/71\/11182288\/11159171.pdf?arnumber=11159171","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T12:23:35Z","timestamp":1759235015000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11159171\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":40,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tpds.2025.3609152","relation":{},"ISSN":["1045-9219","1558-2183","2161-9883"],"issn-type":[{"value":"1045-9219","type":"print"},{"value":"1558-2183","type":"electronic"},{"value":"2161-9883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}