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Novel out-of-core mechanism introduced for large-scale graph neural network trainingIn tests using large-scale real-world graph datasets, Capsule outperformed the best existing systems, achieving up to a 12.02x performance improvement while using only 22.24% of the memory.
Save guides, add subjects and pick up where you left off with your BBC account. A line graph is used to spot trends in data over time. In order to produce a line graph, data is required.
Using a two-sided graph solves this dilemma by displaying the two series in relation to two different vertical scales, one on each side of the graph. Two-sided graphs require three sets of data ...
In general, graphs can have different growth rates at different scales — they might start out fast, and then slow down. Back in 2018, Hutchcroft used a similar idea to prove the locality conjecture ...
Despite its computational advantages offered by GPUs in GNN training, the limited GPU memory capacity struggles to accommodate large-scale graph data, making scalability a significant challenge ...
Experiments are performed to evaluate several different implementations of a classic graph convolution operation by using datasets of different scales, ranging from 0.15 million nodes to 3 million ...
Not All Large-Scale Graphs Are Sparse Many current software systems are designed to study large networks that change over time, but they focus on graphs that are very sparse, meaning that each node in ...
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