@inproceedings{20b99cd81fa94a7392dc5d3a9e0c8983,
title = "SGP: Sampling Big Social Network Based on Graph Partition",
abstract = "Deriving a representative sample from a big social network is essential for many Internet services that rely on accurate analysis of big social data. A good sampling method for social network should be able to generate small sample networks with similar structures as original big network. In this paper, we propose SGP, a new big social network sampling algorithm based on graph partition. In SGP, original network is firstly partitioned into several sub-networks that will be sampled evenly. This procedure enables SGP to effectively maintain the topological similarity and community structure similarity between the sampled network and its original network. We have evaluated SGP on several well-known data sets. The experimental results show that SGP outperforms six state-of-the-art methods.",
keywords = "community structure, graph partition, sampling algorithms, social networks, topology structure",
author = "Xiaolin Du and Yunming Ye and Yan Li and Yueping Li",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Conference on Services Science, ICSS 2015 ; Conference date: 08-05-2015 Through 09-05-2015",
year = "2016",
month = feb,
day = "5",
doi = "10.1109/ICSS.2015.37",
language = "英语",
series = "Proceedings of International Conference on Service Science, ICSS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "205--212",
booktitle = "Proceedings - 2015 International Conference on Services Science, ICSS 2015",
address = "美国",
}