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cs.DC2023
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
Nikolai Merkel, Daniel Stoll, Ruben Mayer +1
Recently, graph neural networks (GNNs) have gained much attention as a growing area of deep learning capable of learning on graph-structured data. However, the computational and me…
cs.DC2023
Partitioner Selection with EASE to Optimize Distributed Graph Processing
Nikolai Merkel, Ruben Mayer, Tawkir Ahmed Fakir +1
For distributed graph processing on massive graphs, a graph is partitioned into multiple equally-sized parts which are distributed among machines in a compute cluster. In the last…