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Graph neural networks (GNNs) are among the most powerful tools in deep learning.
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Mathematical foundations of the graphblas
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High-performance distributed rma locks
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A survey of heterogeneous information network analysis
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Neural message passing for quantum chemistry
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Ambit: In-memory accelerator for bulk bitwise operations using commodity dram technology
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Slim noc: A low-diameter on-chip network topology for high energy efficiency and scalability
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A comprehensive survey of graph embedding: Problems, techniques, and applications
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Fastgcn: fast learning with graph convolutional networks via importance sampling
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Characterizing and understanding gcns on gpu
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Hygcn: A gcn accelerator with hybrid architecture
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Explainability in graph neural networks: A taxonomic survey
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Hardware acceleration of large scale gcn inference
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Transformations of high-level synthesis codes for high-performance computing
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Approximate evaluation of label-constrained reachability queries
S. Dumbrava, A. Bonifati, A. N. R. Diaz, and R. Vuillemot · 2018
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Parallel minimum cuts in near-linear work and low depth
B. Geissmann and L. Gianinazzi · 2018
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Communication-avoiding parallel minimum cuts and connected components
L. Gianinazzi, P. Kalvoda, A. De Palma, M. Besta, and T. Hoefler · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
J. Klicpera et al · 2018
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Shentu: processing multi-trillion edge graphs on millions of cores in seconds
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Deep learning on graphs: A survey
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Distdgl: distributed graph neural network training for billion-scale graphs
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