Abstract
The rapid growth of social networking sites enables people to connect to each other more conveniently than ever. With easy-to-use social media, people contribute and consume contents, leading to a new form of human interaction and the emergence of online collective behavior. In this chapter, we aim to understand group structures and properties by extracting and profiling communities in social media. We present some challenges of community detection in social media. A prominent one is that networks in social media are often heterogeneous. We introduce two types of heterogeneity presented in online social networks and elaborate corresponding community detection approaches for each type, respectively. Social media provides not only interaction information but also textual and tag data. This variety of data can be exploited to profile individual groups in understanding group formation and relationships. We also suggest some future work in understanding group structures and properties.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
N. Agarwal, H. Liu, L. Tang, and P.S. Yu. Identifying the influential bloggers in a community. In WSDM ’08: Proceedings of the international conference on Web search and web data mining. Pages 207–218. ACM, New York, NY, 2008.
L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. Pages 44–54. ACM, New York, NY, 2006.
D. Chakrabarti, and C. Faloutsos. Graph mining: Laws, generators, and algorithms. ACM Computer Survey, 38(1): 2, 2006.
S. Džeroski. Multi-relational data mining: an introduction. SIGKDD Explorations Newsletter, 5(1): 1–16, 2003.
A.T. Fiore, and J.S. Donath. Homophily in online dating: When do you like someone like yourself? In CHI ’05: CHI ’05 extended abstracts on Human factors in computing systems. Pages 1371–1374. ACM, New York, NY, 2005.
D.R. Hardoon, S.R. Szedmak, and J.R. Shawe-taylor. Canonical correlation analysis: An overview with application to learning methods. Neural Computer, 16(12): 2639–2664, 2004.
J. Hopcroft, O. Khan, B. Kulis, and B. Selman. Natural communities in large linked networks. In KDD ’03: Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining. Pages 541–546. ACM, New York, NY, 2003.
H. Kang, L. Getoor, and L. Singh. Visual analysis of dynamic group membership in temporal social networks. SIGKDD Explorations, Special Issue on Visual Analytics, 9(2): 13–21, dec 2007.
J. Kettenring. Canonical analysis of several sets of variables. Biometrika, 58: 433–451, 1971.
J.M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5): 604–632, 1999.
R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. In KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. Pages 611–617. ACM, New York, NY, 2006.
B. Long, Z.M. Zhang, X. Wú, and P.S. Yu. Spectral clustering for multi-type relational data. In ICML ’06: Proceedings of the 23rd international conference on Machine learning. Pages 585–592. ACM, New York, NY, 2006.
U. von Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17(4): 395–416, 2007.
M. McPherson, L. Smith-Lovin, and J.M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27: 415–444, 2001.
M. Newman. Finding community structure in networks using the eigenvectors of matrices. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 74(3), 2006, http://dx.doi.org/10.1103/PhysRevE.74.036104
A. Nielsen. Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data. Image Processing, IEEE Transactions on, 11(3): 293–305, Mar 2002.
K. Nowicki, and T.A.B. Snijders. Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455): 1077–1087, 2001.
L. Tang, and H. Liu. Relational learning via latent social dimensions. In KDD ’09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. Pages 817–826. ACM, New York, NY, 2009.
L. Tang, and H. Liu. Scalable learning of collective behavior based on sparse social dimensions. In CIKM ’09: Proceeding of the 18th ACM conference on Information and knowledge management. Pages 1107–1116. ACM, New York, NY, 2009.
L. Tang, and H. Liu. Uncovering cross-dimension group structures in multi-dimensional networks. In SDM workshop on Analysis of Dynamic Networks, Sparks, NV, 2009.
L. Tang, H. Liu, J. Zhang, N. Agarwal, and J.J. Salerno. Topic taxonomy adaptation for group profiling. ACM Transactions on Knowledge Discovery from Data, 1(4): 1–28, 2008.
L. Tang, H. Liu, J. Zhang, and Z. Nazeri. Community evolution in dynamic multi-mode networks. In KDD ’08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. Pages 677–685. ACM, New York, NY, 2008.
L. Tang, X. Wang, and H. Liu. Uncovering groups via heterogeneous interaction analysis. In Proceeding of IEEE International Conference on Data Mining. Pages 503–512, Miami, FL, 2009.
L. Tang, X. Wang, and H. Liu. Understanding emerging social strucutres: A group-profiling approach. Technical report, Arizona State University, 2010.
L. Tang, J. Zhang, and H. Liu. Acclimatizing taxonomic semantics for hierarchical content classification. In KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. Pages 384–393. ACM, New York, NY, 2006.
M. Thelwall. Homophily in myspace. Journal of the American Society for Information Science and Technology, 60(2): 219–231, 2009.
S. Wasserman, and K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press Cambridge, 1994.
K. Yu, S. Yu, and V. Tresp. Soft clsutering on graphs. In NIPS, Vancouver, Canada, 2005.
D. Zhou, and C.J.C. Burges. Spectral clustering and transductive learning with multiple views. In ICML ’07: Proceedings of the 24th international conference on Machine learning. Pages 1159–1166. ACM, New York, NY, 2007.
Acknowledgments
This work is, in part, supported by ONR and AFOSR.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Tang, L., Liu, H. (2010). Understanding Group Structures and Properties in Social Media. In: Yu, P., Han, J., Faloutsos, C. (eds) Link Mining: Models, Algorithms, and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6515-8_6
Download citation
DOI: https://doi.org/10.1007/978-1-4419-6515-8_6
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-6514-1
Online ISBN: 978-1-4419-6515-8
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)