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 -