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1
IGB: Addressing The Gaps In Labeling, Features, Heterogenei..:
, In:
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
,
Khatua, Arpandeep
;
Mailthody, Vikram Sharma
;
Taleka, Bhagyashree
... - p. 4284-4295 , 2023
Link:
https://dl.acm.org/doi/10.1145/3580305.3599843
RT T1 <cTitle>Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining</cTitle>: T1 <Title>IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3599843&Exemplar=1&LAN=DE A1 Khatua, Arpandeep A1 Mailthody, Vikram Sharma A1 Taleka, Bhagyashree A1 Ma, Tengfei A1 Song, Xiang A1 Hwu, Wen-mei PB ACM YR 2023 K1 datasets K1 deep learning K1 graph neural networks (gnns) K1 graphs K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks K1 Artificial intelligence K1 Natural language processing K1 Knowledge representation and reasoning SP 4284 OP 4295 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3580305.3599843 DO https://dl.acm.org/doi/10.1145/3580305.3599843 SF ELIB - SuUB Bremen
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