TY - GEN
T1 - A Framework for Accelerating Graph Convolutional Networks on Massive Datasets
AU - Li, Xiang
AU - Jin, Ruoming
AU - Ramnath, Rajiv
AU - Agrawal, Gagan
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In recent years, there has been much interest in Graph Convolutional Networks (GCNs). There are several challenges associated with training GCNs. Particularly among them, because of massive scale of graphs, there is not only a large computation time, but also the need for partitioning and loading data multiple times. This paper presents a different framework in which existing GCN methods can be accelerated for execution on large graphs. Building on top of ideas from meta-learning we present an optimization strategy. This strategy is applied to three existing frameworks, resulting in new methods that we refer to as GraphSage++, ClusterGCN++, and GraphSaint++. Using graphs with order of 100 million edges, we demonstrate that we reduce the overall training time by up to 30%, while not having a noticeable reduction in F1 scores in most cases.
AB - In recent years, there has been much interest in Graph Convolutional Networks (GCNs). There are several challenges associated with training GCNs. Particularly among them, because of massive scale of graphs, there is not only a large computation time, but also the need for partitioning and loading data multiple times. This paper presents a different framework in which existing GCN methods can be accelerated for execution on large graphs. Building on top of ideas from meta-learning we present an optimization strategy. This strategy is applied to three existing frameworks, resulting in new methods that we refer to as GraphSage++, ClusterGCN++, and GraphSaint++. Using graphs with order of 100 million edges, we demonstrate that we reduce the overall training time by up to 30%, while not having a noticeable reduction in F1 scores in most cases.
UR - http://www.scopus.com/inward/record.url?scp=85121873803&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121873803&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-91434-9_8
DO - 10.1007/978-3-030-91434-9_8
M3 - Conference contribution
AN - SCOPUS:85121873803
SN - 9783030914332
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 79
EP - 92
BT - Computational Data and Social Networks - 10th International Conference, CSoNet 2021, Proceedings
A2 - Mohaisen, David
A2 - Jin, Ruoming
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th International Conference on Computational Data and Social Networks, CSoNet 2021
Y2 - 15 November 2021 through 17 November 2021
ER -