TY - GEN
T1 - Sampling in online social networks
AU - Kim, Sang Wook
AU - Yoon, Seok Ho
AU - Kim, Ki Nam
AU - Park, Sunju
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 -