TY - GEN
T1 - Deep Graph Clustering with Random-walk based Scalable Learning
AU - Li, Xiang
AU - Li, Dong
AU - Jin, Ruoming
AU - Ramnath, Rajiv
AU - Agrawal, Gagan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Interactions between (social) entities can be frequently represented by an attributed graph, and node clustering in such graphs has received much attention lately. Multiple efforts have successfully applied Graph Convolutional Networks (GCN), though with some limits on accuracy as GCNs have been shown to suffer from over-smoothing issues. Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability. This paper addresses this open problem by relating the Laplacian smoothing to the Generalized PageRank, and applying a random-walk based algorithm as a scalable graph filter. This forms the basis for our scalable deep clustering algorithm, RwSL. Using 6 real-world datasets and 6 clustering metrics, we show that RwSL achieved improved results over several recent baselines. Most notably, by demonstrating execution of RwSL on a graph with 1.8 billion edges using only a single GPU. We show that RwSL can continue to scale, unlike other existing deep clustering frameworks.
AB - Interactions between (social) entities can be frequently represented by an attributed graph, and node clustering in such graphs has received much attention lately. Multiple efforts have successfully applied Graph Convolutional Networks (GCN), though with some limits on accuracy as GCNs have been shown to suffer from over-smoothing issues. Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability. This paper addresses this open problem by relating the Laplacian smoothing to the Generalized PageRank, and applying a random-walk based algorithm as a scalable graph filter. This forms the basis for our scalable deep clustering algorithm, RwSL. Using 6 real-world datasets and 6 clustering metrics, we show that RwSL achieved improved results over several recent baselines. Most notably, by demonstrating execution of RwSL on a graph with 1.8 billion edges using only a single GPU. We show that RwSL can continue to scale, unlike other existing deep clustering frameworks.
KW - Attributed Graph
KW - Deep Clustering
KW - Generalized PageRank
KW - Graph Convolutional Network
KW - Laplacian Smoothing
UR - http://www.scopus.com/inward/record.url?scp=85151961167&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85151961167&partnerID=8YFLogxK
U2 - 10.1109/ASONAM55673.2022.10068646
DO - 10.1109/ASONAM55673.2022.10068646
M3 - Conference contribution
AN - SCOPUS:85151961167
T3 - Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
SP - 88
EP - 95
BT - Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
A2 - An, Jisun
A2 - Charalampos, Chelmis
A2 - Magdy, Walid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
Y2 - 10 November 2022 through 13 November 2022
ER -