{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:14:42Z","timestamp":1774026882294,"version":"3.50.1"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005015","name":"South China University of Technology","doi-asserted-by":"publisher","award":["K3200890"],"award-info":[{"award-number":["K3200890"]}],"id":[{"id":"10.13039\/501100005015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24B20151"],"award-info":[{"award-number":["U24B20151"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.neucom.2026.132978","type":"journal-article","created":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T07:40:06Z","timestamp":1770450006000},"page":"132978","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["CaPGNN: Optimizing parallel graph neural network training with joint caching and resource-aware graph partitioning"],"prefix":"10.1016","volume":"675","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7369-1821","authenticated-orcid":false,"given":"Xianfeng","family":"Song","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4382-4670","authenticated-orcid":false,"given":"Yi","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Zheng","family":"Shi","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.132978_bib0005","author":"Ai"},{"key":"10.1016\/j.neucom.2026.132978_bib0010","series-title":"International Conference on Learning Representations","article-title":"Deep Gaussian embedding of graphs: unsupervised inductive learning via ranking","author":"Bojchevski","year":"2018"},{"key":"10.1016\/j.neucom.2026.132978_bib0015","series-title":"Proceedings of the Sixteenth European Conference on Computer Systems","first-page":"130","article-title":"Dgcl: an efficient communication library for distributed GNN training","author":"Cai","year":"2021"},{"key":"10.1016\/j.neucom.2026.132978_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298989","article-title":"Powerlyra: differentiated graph computation and partitioning on skewed graphs","volume":"5","author":"Chen","year":"2019","journal-title":"ACM Trans. Parallel Comput."},{"key":"10.1016\/j.neucom.2026.132978_bib0025","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"257","article-title":"Cluster-gcn: an efficient algorithm for training deep and large graph convolutional networks","author":"Chiang","year":"2019"},{"key":"10.1016\/j.neucom.2026.132978_bib0030","series-title":"International Conference on Machine Learning","first-page":"1263","article-title":"Neural message passing for quantum Chemistry","author":"Gilmer","year":"2017"},{"key":"10.1016\/j.neucom.2026.132978_bib0035","series-title":"Proceedings of the 49th Annual International Symposium on Computer Architecture","first-page":"916","article-title":"Graphite: optimizing graph neural networks on cpus through cooperative software-hardware techniques","author":"Gong","year":"2022"},{"key":"10.1016\/j.neucom.2026.132978_bib0040","series-title":"10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 12)","first-page":"17","article-title":"{PowerGraph}: distributed{Graph-Parallel} computation on natural graphs","author":"Gonzalez","year":"2012"},{"key":"10.1016\/j.neucom.2026.132978_bib0045","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/MCI.2020.3039072","article-title":"Graph neural networks in tensorflow and keras with spektral [application notes]","volume":"16","author":"Grattarola","year":"2021","journal-title":"IEEE Comput. Intell. Mag."},{"key":"10.1016\/j.neucom.2026.132978_bib0050","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.132978_bib0055","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.132978_bib0060","first-page":"22118","article-title":"Open graph benchmark: datasets for machine learning on graphs","volume":"33","author":"Hu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.132978_bib0065","series-title":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","first-page":"1","article-title":"Ge-spmm: general-purpose sparse matrix-matrix multiplication on gpus for graph neural networks","author":"Huang","year":"2020"},{"key":"10.1016\/j.neucom.2026.132978_bib0070","series-title":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","first-page":"1","article-title":"Ge-spmm: general-purpose sparse matrix-matrix multiplication on gpus for graph neural networks","author":"Huang","year":"2020"},{"key":"10.1016\/j.neucom.2026.132978_bib0075","doi-asserted-by":"crossref","first-page":"46796","DOI":"10.1109\/ACCESS.2022.3169423","article-title":"Practical near-data-processing architecture for large-scale distributed graph neural network","volume":"10","author":"Huang","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.132978_bib0080","first-page":"187","article-title":"Improving the accuracy, scalability, and performance of graph neural networks with ROC","volume":"2","author":"Jia","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"10.1016\/j.neucom.2026.132978_bib0085","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1137\/S1064827595287997","article-title":"A fast and high quality multilevel scheme for partitioning irregular graphs","volume":"20","author":"Karypis","year":"1998","journal-title":"SIAM J. Sci. Comput."},{"key":"10.1016\/j.neucom.2026.132978_bib0090","first-page":"0","article-title":"Metis: a software package for partitioning unstructured graphs","volume":"4","author":"Karypis","year":"1998","journal-title":"Partitioning Meshes, and Computing Fill-Reducing Orderings of Sparse Matrices, Version"},{"key":"10.1016\/j.neucom.2026.132978_bib0095","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11313","article-title":"Semi-supervised learning with graph learning-convolutional networks","author":"Jiang","year":"2019"},{"key":"10.1016\/j.neucom.2026.132978_bib0100","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/TPDS.2019.2928289","article-title":"Evaluating modern GPU interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect","volume":"31","author":"Li","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"12","key":"10.1016\/j.neucom.2026.132978_bib0105","doi-asserted-by":"crossref","first-page":"3073","DOI":"10.1109\/TPDS.2023.3313779","article-title":"Task placement and resource allocation for edge machine learning: a gnn-based multi-agent reinforcement learning paradigm","volume":"34","author":"Li","year":"2023","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.neucom.2026.132978_bib0110","series-title":"Proceedings of the 11th ACM Symposium on Cloud Computing","first-page":"401","article-title":"Pagraph: scaling GNN training on large graphs via computation-aware caching","author":"Lin","year":"2020"},{"key":"10.1016\/j.neucom.2026.132978_bib0115","series-title":"20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)","first-page":"103","article-title":"BGL:GPU-EfficientGNN training by optimizing graph data i\/o and preprocessing","author":"Liu","year":"2023"},{"key":"10.1016\/j.neucom.2026.132978_bib0120","series-title":"2019 USENIX Annual Technical Conference (USENIX ATC 19)","first-page":"443","article-title":"{NeuGraph}: parallel deep neural network computation on large graphs","author":"Ma","year":"2019"},{"key":"10.1016\/j.neucom.2026.132978_bib0125","series-title":"Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data","first-page":"135","article-title":"Pregel: a system for large-scale graph processing","author":"Malewicz","year":"2010"},{"key":"10.1016\/j.neucom.2026.132978_bib0130","author":"Merkel"},{"key":"10.1016\/j.neucom.2026.132978_bib0135","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1038\/s41586-021-03544-w","article-title":"A graph placement methodology for fast chip design","volume":"594","author":"Mirhoseini","year":"2021","journal-title":"Nature"},{"key":"10.1016\/j.neucom.2026.132978_bib0140","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.14778\/3538598.3538614","article-title":"Sancus: STA le n ess-aware c omm u nication-avoiding full-graph decentralized training in large-scale graph neural networks","volume":"15","author":"Peng","year":"2022","journal-title":"Proc. VLDB Endow."},{"key":"10.1016\/j.neucom.2026.132978_bib0145","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.14778\/3538598.3538614","article-title":"Sancus: staleness-aware communication-avoiding full-graph decentralized training in large-scale graph neural networks","volume":"15","author":"Peng","year":"2022","journal-title":"Proc. VLDB Endow."},{"key":"10.1016\/j.neucom.2026.132978_bib0150","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4938","article-title":"Superglue: learning feature matching with graph neural networks","author":"Sarlin","year":"2020"},{"key":"10.1016\/j.neucom.2026.132978_bib0155","series-title":"The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings 15","first-page":"593","article-title":"Modeling relational data with graph convolutional networks","author":"Schlichtkrull","year":"2018"},{"key":"10.1016\/j.neucom.2026.132978_bib0160","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3648358","article-title":"Distributed graph neural network training: a survey","volume":"56","author":"Shao","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.neucom.2026.132978_bib0165","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3661821","article-title":"A survey of graph neural networks for social recommender systems","volume":"56","author":"Sharma","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.neucom.2026.132978_bib0170","author":"Shchur"},{"key":"10.1016\/j.neucom.2026.132978_bib0175","doi-asserted-by":"crossref","first-page":"2624","DOI":"10.1109\/TPDS.2024.3488053","article-title":"Dylaclass: dynamic labeling based classification for optimal sparse matrix format selection in accelerating spmv","volume":"35","author":"Shi","year":"2024","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.neucom.2026.132978_bib0180","series-title":"2023 USENIX Annual Technical Conference","first-page":"165","article-title":"Legion: automatically pushing the envelope of{multi-GPU} system for {Billion-Scale }{ GNN}training","author":"Sun","year":"2023"},{"key":"10.1016\/j.neucom.2026.132978_bib0185","series-title":"Proceedings of the 7th ACM International Conference on Web Search and Data Mining","first-page":"333","article-title":"Fennel: streaming graph partitioning for massive scale graphs","author":"Tsourakakis","year":"2014"},{"key":"10.1016\/j.neucom.2026.132978_bib0190","author":"Veli\u010dkovi\u0107"},{"key":"10.1016\/j.neucom.2026.132978_bib0195","doi-asserted-by":"crossref","first-page":"493","DOI":"10.14778\/3055540.3055543","article-title":"An experimental comparison of partitioning strategies in distributed graph processing","volume":"10","author":"Verma","year":"2017","journal-title":"Proc. VLDB Endow."},{"key":"10.1016\/j.neucom.2026.132978_bib0200","article-title":"Adaptive message quantization and parallelization for distributed full-graph GNN training","volume":"5","author":"Wan","year":"2023","journal-title":"Proc. Mach. Learn. Syst."},{"key":"10.1016\/j.neucom.2026.132978_bib0205","article-title":"Adaptive message quantization and parallelization for distributed full-graph GNN training","volume":"5","author":"Wan","year":"2023","journal-title":"Proc. Mach. Learn. Syst."},{"key":"10.1016\/j.neucom.2026.132978_bib0210","first-page":"673","article-title":"Bns-gcn: efficient full-graph training of graph convolutional networks with partition-parallelism and random boundary node sampling","volume":"4","author":"Wan","year":"2022","journal-title":"Proc. Mach. Learn. Syst."},{"key":"10.1016\/j.neucom.2026.132978_bib0215","series-title":"The Tenth International Conference on Learning Representations (ICLR 2022)","article-title":"PipeGCN: efficient full-graph training of graph convolutional networks with pipelined feature communication","author":"Wan","year":"2022"},{"key":"10.1016\/j.neucom.2026.132978_bib0220","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3589288","article-title":"Scalable and efficient full-graph GNN training for large graphs","volume":"1","author":"Wan","year":"2023","journal-title":"Proc. ACM Manag. Data"},{"key":"10.1016\/j.neucom.2026.132978_bib0225","first-page":"1","article-title":"Hongtu: scalable full-graph GNN training on multiple gpus","volume":"1","author":"Wang","year":"2023","journal-title":"Proc. ACM Manag. Data"},{"key":"10.1016\/j.neucom.2026.132978_bib0230","series-title":"Proceedings of the 2022 International Conference on Management of Data","first-page":"1301","article-title":"Neutronstar: distributed GNN training with hybrid dependency management","author":"Wang","year":"2022"},{"key":"10.1016\/j.neucom.2026.132978_bib0235","first-page":"1","article-title":"Graph neural networks in recommender systems: a survey","volume":"55","author":"Wu","year":"2022","journal-title":"ACM Comput. Surv."},{"issue":"11","key":"10.1016\/j.neucom.2026.132978_bib0240","doi-asserted-by":"crossref","first-page":"3167","DOI":"10.1109\/TC.2023.3288755","article-title":"Sugar: efficient subgraph-level training via resource-aware graph partitioning","volume":"72","author":"Xue","year":"2023","journal-title":"IEEE Trans. Comput."},{"key":"10.1016\/j.neucom.2026.132978_bib0245","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"3165","article-title":"Aligraph: a comprehensive graph neural network platform","author":"Yang","year":"2019"},{"key":"10.1016\/j.neucom.2026.132978_bib0250","first-page":"28798","article-title":"Graphformers: gnn-nested transformers for representation learning on textual graph","volume":"34","author":"Yang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.132978_bib0255","series-title":"Proceedings of the Seventeenth European Conference on Computer Systems","first-page":"417","article-title":"Gnnlab: a factored system for sample-based GNN training over gpus","author":"Yang","year":"2022"},{"key":"10.1016\/j.neucom.2026.132978_bib0260","doi-asserted-by":"crossref","first-page":"75729","DOI":"10.1109\/ACCESS.2022.3191784","article-title":"A comprehensive survey of graph neural networks for knowledge graphs","volume":"10","author":"Ye","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.132978_bib0265","series-title":"International Conference on Learning Representations","article-title":"GraphSAINT: graph sampling based inductive learning method","author":"Zeng","year":"2020"},{"key":"10.1016\/j.neucom.2026.132978_bib0270","article-title":"Windgp: efficient graph partitioning on heterogenous machines","volume":"abs\/2403.00331","author":"Zeng","year":"2024","journal-title":"CoRR"},{"key":"10.1016\/j.neucom.2026.132978_bib0275","article-title":"CDFGNN: a systematic design of cache-based distributed full-batch graph neural network training with communication reduction","volume":"abs\/2408.00232","author":"Zhang","year":"2024","journal-title":"CoRR"},{"key":"10.1016\/j.neucom.2026.132978_bib0280","series-title":"IEEE INFOCOM 2023-IEEE Conference on Computer Communications","first-page":"1","article-title":"Two-level graph caching for expediting distributed GNN training","author":"Zhang","year":"2023"},{"key":"10.1016\/j.neucom.2026.132978_bib0285","series-title":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","first-page":"3310","article-title":"Neutroncache: an efficient cache-enhanced distributed graph neural network training system","author":"Zhao","year":"2024"},{"key":"10.1016\/j.neucom.2026.132978_bib0290","series-title":"2020 IEEE\/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3)","first-page":"36","article-title":"Distdgl: distributed graph neural network training for billion-scale graphs","author":"Zheng","year":"2020"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226003759?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226003759?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:24:39Z","timestamp":1774023879000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226003759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":58,"alternative-id":["S0925231226003759"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.132978","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CaPGNN: Optimizing parallel graph neural network training with joint caching and resource-aware graph partitioning","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.132978","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132978"}}