TY - GEN

T1 - Sampling in online social networks

AU - Kim, Sang Wook

AU - Yoon, Seok Ho

AU - Kim, Ki Nam

AU - Park, Sunju

N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.

PY - 2014

Y1 - 2014

N2 - In this paper, we propose a new graph sampling method for online social networks that achieves the following. First, a sample graph should reflect the ratio between the number of nodes and the number of edges of the original graph. Second, a sample graph should reflect the topology of the original graph. Third, sample graphs should be consistent with each other when they are sampled from the same original graph. The proposed method employs two techniques: hierarchical community extraction and densification power law. The proposed method partitions the original graph into a set of communities to preserve the topology of the original graph. It also uses the densification power law which captures the ratio between the number of nodes and the number of edges in online social networks. In experiments, we use several real-world online social networks, create sample graphs using the existing methods and ours, and analyze the differences between the sample graph by each sampling method and the original graph.

AB - In this paper, we propose a new graph sampling method for online social networks that achieves the following. First, a sample graph should reflect the ratio between the number of nodes and the number of edges of the original graph. Second, a sample graph should reflect the topology of the original graph. Third, sample graphs should be consistent with each other when they are sampled from the same original graph. The proposed method employs two techniques: hierarchical community extraction and densification power law. The proposed method partitions the original graph into a set of communities to preserve the topology of the original graph. It also uses the densification power law which captures the ratio between the number of nodes and the number of edges in online social networks. In experiments, we use several real-world online social networks, create sample graphs using the existing methods and ours, and analyze the differences between the sample graph by each sampling method and the original graph.

UR - http://www.scopus.com/inward/record.url?scp=84905663938&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84905663938&partnerID=8YFLogxK

U2 - 10.1145/2554850.2554907

DO - 10.1145/2554850.2554907

M3 - Conference contribution

AN - SCOPUS:84905663938

SN - 9781450324694

T3 - Proceedings of the ACM Symposium on Applied Computing

SP - 845

EP - 849

BT - Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014

PB - Association for Computing Machinery

T2 - 29th Annual ACM Symposium on Applied Computing, SAC 2014

Y2 - 24 March 2014 through 28 March 2014

ER -