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Deep Learning on Graphs: Methods and Applications (DLG-KDD2..:

, In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,

Wu, Lingfei ; Pei, Jian ; Tang, Jiliang.. - p. 5891-5892 , 2023

Link: https://dl.acm.org/doi/10.1145/3580305.3599207

RT 
T1 <cTitle>Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining</cTitle>: T1 <Title>Deep Learning on Graphs: Methods and Applications (DLG-KDD2023)</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3599207&Exemplar=1&LAN=DE
				
A1 Wu, Lingfei
A1 Pei, Jian
A1 Tang, Jiliang
A1 Xia, Yinglong
A1 Guo, Xiaojie
PB ACM
YR 2023
K1 deep learning
K1 graph mining.
K1 graph neural network
K1 graph representation learn- ing
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Unsupervised learning
K1 Machine learning approaches
K1 Neural networks
K1 Information systems
K1 Information systems applications
K1 Data mining
K1 Mathematics of computing
K1 Discrete mathematics
K1 Graph theory
K1 Graph algorithms
K1 Networks
K1 Network properties
K1 Network structure
K1 Topology analysis and generation
K1 Bio-inspired approaches
K1 Generative and developmental approaches
SP 5891
OP 5892
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3580305.3599207
DO https://dl.acm.org/doi/10.1145/3580305.3599207
SF ELIB - SuUB Bremen

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