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pro vyhledávání: '"Polisetty, Sandeep"'
Graph pattern matching is a fundamental problem encountered by many common graph mining tasks and the basic building block of several graph mining systems. This paper explores for the first time how to proactively prune graphs to speed up graph patte
Externí odkaz:
http://arxiv.org/abs/2403.01050
Autor:
Polisetty, Sandeep, Liu, Juelin, Falus, Kobi, Fung, Yi Ren, Lim, Seung-Hwan, Guan, Hui, Serafini, Marco
Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their superior performance in various graph analytical tasks. Mini-batch training is commonly used to train GNNs on large graphs, and da
Externí odkaz:
http://arxiv.org/abs/2303.13775
Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and GraphSAGE, sample the graph to produce mini-batches that are suitable for training
Externí odkaz:
http://arxiv.org/abs/2009.06693