Accelerating graph sampling for graph machine learning using GPUs
Abhinav Jangda, Sandeep Polisetty, Arjun Guha, Marco Serafini
Abstract
Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and Graph-SAGE, sample the graph to produce mini-batches that are suitable for training a DNN. However, sampling time can be a significant fraction of training time, and existing systems do not efficiently parallelize sampling.
Topics & Concepts
Computer scienceGraphTheoretical computer scienceArtificial intelligenceExternal Data RepresentationSampling (signal processing)Representation (politics)Machine learningComputer visionPolitical scienceFilter (signal processing)LawPoliticsAdvanced Graph Neural NetworksGraph Theory and AlgorithmsComplexity and Algorithms in Graphs