Integrating social media data for community detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7472 LNAI, Issue , 2012, Pages 1-20

  Tang, Jiliang ^a   Wang, Xufei ^a   Liu, Huan ^a  

^a Arizona State University   (United States)

Author keywords

Community Detection; Multi source Integration; Social Media Data

Indexed keywords

BOOKMARKING; COMMUNITY DETECTION; EMPIRICAL EVALUATIONS; GROUP MEMBERS; HETEROGENEOUS DATA; JOINT OPTIMIZATION; LINK INFORMATIONS; MULTI-SOURCE INTEGRATIONS; MULTIPLE DATA SOURCES; MULTISOURCES; PERFORMANCE IMPROVEMENTS; SOCIAL MEDIA; STRENGTH OF TIES; SYNTHETIC DATA; USER INTERESTS;

ARTIFICIAL INTELLIGENCE;

POPULATION DYNAMICS;

EID: 84867631568     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33684-3_1     Document Type: Conference Paper

References (25)

1. Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: KDD, pp. 44-54. ACM (2006)
2. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993-1022 (2003)
3. Chaudhuri, K., Kakade, S.M., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: ICML (2009)
4. Erosheva, E., Fienberg, S., Lafferty, J.: Mixed-membership models of scientific publications. Proceedings of the National Academy of Sciences of the United States of America 101(suppl. 1), 5220 (2004)
5. Evans, T., Lambiotte, R.: Line graphs, link partitions, and overlapping communities. Physical Review E 80(1), 16105 (2009)
6. Lin, Y.-R., Sun, J., Castro, P., Konuru, R., Sundaram, H., Kelliher, A.: Metafac: community discovery via relational hypergraph factorization. In:KDD, pp. 527-536. ACM (2009)
7. Liu, Y., Niculescu-Mizil, A., Gryc, W.: Topic-link lda: Joint models of topic and author community. In: ICML 2009 (2009)
8. Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395-416 (2007)
9. McPherson, M., Lovin, L.S., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27(1), 415-444 (2001)
10. Newman, M.E.: Finding community structure in networks using the eigenvectors of matrices. Physical Review E 74(3), 36104 (2006)
11. Newman, M.E., Leicht, E.: Mixture models and exploratory analysis in networks. Proceedings of the National Academy of Sciences 104(23), 9564 (2007)
12. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69(2), 26113 (2004)
13. Palla, G., Dernyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814-818 (2005)
14. Scellato, S., Mascolo, C., Musolesi, M., Latora, V.: Distance matters: Geo-social metrics for online social networks. In: WOSN 2010 (2010)
15. Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3, 583-617 (2002)
16. Tang, J., Gao, H., Liu, H.: mtrust: discerning multi-faceted trust in a connected world. In: WSDM, pp. 93-102. ACM (2012)
17. Tang, J., Liu, H.: Feature selection with linked data in social media. In: SDM (2012)
18. Tang, J., Liu, H.: Unsupervised feature selection for linked social media data. In: KDD (2012)
19. Tang, L., Liu, H.: Scalable learning of collective behavior based on sparse social dimensions. In: CIKM, pp. 1107-1116. ACM (2009)
20. Tang, L., Wang, X., Liu, H.: Uncovering groups via heterogeneous interaction analysis. In: ICDM, Miami, FL, USA, December 6-9 (2009)
21. Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B 58(1), 267-288 (1996)
22. Wang, X., Tang, L., Gao, H., Liu, H.: Discovering overlapping groups in social media. In: ICDM, Sydney, Australia, December 14-17 (2010)
23. White, S., Smyth, P.: A spectral clustering approach to finding communities in graphs. In: SDM, p. 274. Society for Industrial Mathematics (2005)
24. Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: WWW, pp. 981-990. ACM (2010)
25. Yang, T., Jin, R., Chi, Y., Zhu, S.: Combining link and content for community detection: a discriminative approach. In: KDD, pp. 927-936. ACM (2009)