Sampling in online social networks
Pages 845 - 849
Abstract
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.
References
[1]
R. Kumar, J. Novak, and A Tomkins, "Structure and Evolution of Online Social Networks," In Proc. of Int'l. Conf. on Knowledge Discovery and Data, KDD, pp. 611--617, 2006.
[2]
S. Das, O. Egecioglu, and A. El Abbadi, "Anonimos: An LP-Based Approach for Anonymizing Weighted Social Network Graphs," IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 4, pp. 590--604, 2012.
[3]
S. H. Yoon et al., "Extraction of a Latent Blog Community Based on Subject," In Proc. of ACM Int Int'l. Conf. on Information and Knowledge Management, ACM CIKM, pp. 1529--1532, 2009.
[4]
J. Leskovec, J. Kleinberg, and C. Faloutsos, "Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations," In Proc. of ACM Int'l. Conf. on Knowledge Discovery and Data Mining, ACM SIGKDD, pp. 177--187, 2005.
[5]
J. Leskovec and C. Faloutsos, "Sampling from Large Graphs," In Proc. of ACM Int'l. Conf. on Knowledge Discovery and Data Mining, ACM SIGKDD, pp. 631--636, 2006.
[6]
H C. Hübler et al., "Metropolis Algorithms for Representative Subgraph Sampling," In Proc. of IEEE Int'l. Conf. on Data Mining, ICDM, pp. 283--292, 2008.
[7]
S. Lee, P. Kim, and H. Jeong, "Statistical Properties of Sampled Networks," Physical Review E, Vol. 73, 2006.
[8]
A. Clauset, M. Newman, and C. Moore, "Finding Community Structure in Very Large Networks," Physical Review E, Vol. 70, 2004.
[9]
A. Barabasi, "Linked: The New Science of Networks," American Journal of Physics, Vol. 71, pp. 409, 2003.
[10]
D. Chakrabarti et al., "Fully Automatic Cross-Associations," In Proc. Int'l Conf. on Knowledge Discovery and Data Mining, KDD, pp. 79--88, 2004.
[11]
N. Friedman and S. Russell, "Image Segmentation in Video Sequences: A Probabilistic Approach," In Proc. 13th Conf. Uncertainty in Artificial Intelligence, 1997.
[12]
G. Karypis, E. H. Han, and V. Kumar, "Chameleon: A Hierarchical Clustering Algorithm Using Dynamic Modeling," IEEE Computer, Vol. 32, No. 8, pp. 68--75, 1999.
Index Terms
- Sampling in online social networks
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Published In
March 2014
1890 pages
ISBN:9781450324694
DOI:10.1145/2554850
- Conference Chairs:
- Yookun Cho,
- Sung Y. Shin,
- Program Chairs:
- Sangwook Kim,
- Chih-Cheng Hung,
- Jiman Hong
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Published: 24 March 2014
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