.png){kind=icon}
Return
Cluster-preserving sampling algorithm for large-scale graphs
.png){kind=icon}
PRE
AI
DOI：10.1007/s11432-021-3370-4.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Graph sampling is a very effective method to deal with scalability issues when analyzing large-scale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties (e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods (i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.
Keywords:
graph sampling
clustering structure
top-leader nodes
expansion strategies
large-scale graphs
Journal
IF:
7.6
Papers:
4.9K
Citations:
8.9K
Organization
Cited Papers
Soluble PD‐1 rescues the proliferative response of simian immunodeficiency virus‐specific CD4 and CD8 T cells during chronic infection.png)
Immunology
OAAI
ImmunologyIF0
OAAIShareSaveShareSave
ShareSaveStudy on the Progressive Failure Characteristics of Coal in Uniaxial and Triaxial Compression Conditions Using 3D-Digital Image CorrelationEnergiesOAAI
EnergiesIF0
OAAIShareSaveShareSave
View more