TY - GEN
T1 - SGCN
T2 - 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
AU - Li, Jiayu
AU - Zhang, Tianyun
AU - Tian, Hao
AU - Jin, Shengmin
AU - Fardad, Makan
AU - Zafarani, Reza
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Graphs are ubiquitous across the globe and within science and engineering. With graphs growing in size, node classification on large graphs can be space and time consuming, even with powerful classifiers such as Graph Convolutional Networks (GCNs). Hence, some questions are raised, particularly, whether one can keep only some of the edges of a graph while maintaining prediction performance for node classification, or train classifiers on specific subgraphs instead of a whole graph with limited performance loss in node classification. To address these questions, we propose Sparsified Graph Convolutional Network (SGCN), a neural network graph sparsifier that sparsifies a graph by pruning some edges. We formulate sparsification as an optimization problem, which we solve by an Alternating Direction Method of Multipliers (ADMM)-based solution. We show that sparsified graphs provided by SGCN can be used as inputs to GCN, leading to better or comparable node classification performance with that of original graphs in GCN, DeepWalk, and GraphSAGE.
AB - Graphs are ubiquitous across the globe and within science and engineering. With graphs growing in size, node classification on large graphs can be space and time consuming, even with powerful classifiers such as Graph Convolutional Networks (GCNs). Hence, some questions are raised, particularly, whether one can keep only some of the edges of a graph while maintaining prediction performance for node classification, or train classifiers on specific subgraphs instead of a whole graph with limited performance loss in node classification. To address these questions, we propose Sparsified Graph Convolutional Network (SGCN), a neural network graph sparsifier that sparsifies a graph by pruning some edges. We formulate sparsification as an optimization problem, which we solve by an Alternating Direction Method of Multipliers (ADMM)-based solution. We show that sparsified graphs provided by SGCN can be used as inputs to GCN, leading to better or comparable node classification performance with that of original graphs in GCN, DeepWalk, and GraphSAGE.
KW - Graph convolutional network
KW - Graph sparsification
KW - Node classification
UR - http://www.scopus.com/inward/record.url?scp=85085735593&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085735593&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-47426-3_22
DO - 10.1007/978-3-030-47426-3_22
M3 - Conference contribution
AN - SCOPUS:85085735593
SN - 9783030474256
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 275
EP - 287
BT - Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings
A2 - Lauw, Hady W.
A2 - Lim, Ee-Peng
A2 - Wong, Raymond Chi-Wing
A2 - Ntoulas, Alexandros
A2 - Ng, See-Kiong
A2 - Pan, Sinno Jialin
PB - Springer
Y2 - 11 May 2020 through 14 May 2020
ER -