TY - GEN
T1 - Two-Level Graph Representation Learning with Community-as-a-Node Graphs
AU - Park, Jeong Ha
AU - Lee, Kisung
AU - Kwon, Hyuk Yoon
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we propose a novel graph representation learning (GRL) model that aims to improve both representation accuracy and learning efficiency. We design a Two-Level GRL architecture based on the graph partitioning: 1) local GRL on nodes within each partitioned subgraph and 2) global GRL on subgraphs. By partitioning the graph through community detection, we enable elaborate node learning in the same community. Based on Two-Level GRL, we introduce an abstracted graph, Community-as-a-Node Graph(CaaN), to effectively maintain the high-level structure with a significantly reduced graph. By applying the CaaN graph to local and global GRL, we propose Two-Level GRL with Community-as-a-Node (CaaN 2L) that effectively maintains the global structure of the entire graph while accurately representing the nodes in each community. A salient point of the proposed model is that it can be applied to any existing GRL model by adopting it as the base model for local and global GRL. Through extensive experiments employing seven popular GRL models, we show that our model outperforms them in both accuracy and efficiency.
AB - In this paper, we propose a novel graph representation learning (GRL) model that aims to improve both representation accuracy and learning efficiency. We design a Two-Level GRL architecture based on the graph partitioning: 1) local GRL on nodes within each partitioned subgraph and 2) global GRL on subgraphs. By partitioning the graph through community detection, we enable elaborate node learning in the same community. Based on Two-Level GRL, we introduce an abstracted graph, Community-as-a-Node Graph(CaaN), to effectively maintain the high-level structure with a significantly reduced graph. By applying the CaaN graph to local and global GRL, we propose Two-Level GRL with Community-as-a-Node (CaaN 2L) that effectively maintains the global structure of the entire graph while accurately representing the nodes in each community. A salient point of the proposed model is that it can be applied to any existing GRL model by adopting it as the base model for local and global GRL. Through extensive experiments employing seven popular GRL models, we show that our model outperforms them in both accuracy and efficiency.
KW - community detection
KW - graph representation learning
KW - learning efficiency
KW - representation accuracy
UR - http://www.scopus.com/inward/record.url?scp=85185404780&partnerID=8YFLogxK
U2 - 10.1109/ICDM58522.2023.00158
DO - 10.1109/ICDM58522.2023.00158
M3 - Conference contribution
AN - SCOPUS:85185404780
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1259
EP - 1264
BT - Proceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023
A2 - Chen, Guihai
A2 - Khan, Latifur
A2 - Gao, Xiaofeng
A2 - Qiu, Meikang
A2 - Pedrycz, Witold
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Data Mining, ICDM 2023
Y2 - 1 December 2023 through 4 December 2023
ER -