Leveraging social media networks for classification

Airodi EM, Blei D, Fienberg SE, Xing EP (2008) Mixed membership stochastic block models. J Mach Learn Res 9: 1981–2014 Almack JC (1922) The influence of intelligence on the selection of associates. Sch Soc 16: 529–530 Bott H (1928) Observation of play activities in a nursery school. Genet Psychol Monogr 4: 44–88 Chakrabarti D, Faloutsos C (2006) Graph mining: laws, generators, and algorithms. ACM Comput Surv 38(1): 2 Chakrabarti S, Dom B, Indyk P (1998) Enhanced hypertext categorization using hyperlinks. In: SIGMOD ’98: proceedings of the 1998 ACM SIGMOD international conference on management of data. ACM, New York, NY, USA, pp 307–318 Chang E, Zhu K, Wang H, Bai H, Li J, Qiu Z, Cui H (2007) Psvm: parallelizing support vector machines on distributed computers. Adv Neural Inf Process Syst 20: 1081–1088 Chen G, Wang F, Zhang C (2008) Semi-supervised multi-label learning by solving a sylvester equation. In: Proceedings of the SIAM international conference on data mining, Bethesda, MD, USA, pp 410–419 Chen W-Y, Song Y, Bai H, Lin C-J, Chang EY (2010) Parallel spectral clustering in distributed systems. IEEE Trans Pattern Anal Mach Intell 99 Fan R-E, Lin C-J (2007) A study on threshold selection for multi-label classication. Technical report, National Taiwan University Fiore AT, Donath JS (2005) Homophily in online dating: when do you like someone like yourself?. In: CHI ’05: CHI ’05 extended abstracts on human factors in computing systems. ACM, New York, NY, USA, pp 1371–1374 Fortunato S, Barthelemy M (2007) Resolution limit in community detection. PNAS 104(1): 36–41 Gallagher B, Tong H, Eliassi-Rad T, Faloutsos C (2008) Using ghost edges for classification in sparsely labeled networks. In: KDD ’08: proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, USA, pp 256–264 Geman S, Geman D (1990) Stochastic relaxation, gibbs distributions, and the bayesian restoration of images, San Francisco, CA, USA, pp 452–472 Getoor L, Taskar B (Eds) (2007) Introduction to statistical relational learning. The MIT Press, London, England Golub GH, Van Loan CF (1996) Matrix computations. 3. Johns Hopkins University Press, Baltimore Graf H, Cosatto E, Bottou L, Dourdanovic I, Vapnik V (2005) Parallel support vector machines: the cascade svm. Adv Neural Inf Process Syst 17(521-528): 2 Handcock MS, Raftery AE, Tantrum JM. (2007) Model-based clustering for social networks. J R Stat Soc A 127(2): 301–354 Hoff PD, Raftery AE, Handcock MS (2002) Latent space approaches to social network analysis. J A Stat Assoc 97(460): 1090–1098 Hopcroft J, Khan O, Kulis B, Selman B (2003) Natural communities in large linked networks. In: KDD ’03: proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, USA, pp 541–546 Jensen D, Neville J, Gallagher B (2004) Why collective inference improves relational classification. In: KDD ’04: proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, USA, pp 593–598 Kondor RI, Lafferty J (2002) Diffusion kernels on graphs and other discrete structures. In: ICML, New York, NY, USA Kumar R, Novak J, Tomkins A (2006) Structure and evolution of online social networks. In: KDD ’06: proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, USA, pp 611–617 Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2008) Statistical properties of community structure in large social and information networks. In: WWW ’08: proceeding of the 17th international conference on world wide web. ACM, New York, NY, USA, pp 695–704 Leskovec J, Lang KJ, Mahoney M (2010) Empirical comparison of algorithms for network community detection. In: WWW ’10: proceedings of the 19th international conference on World wide web. ACM, New York, NY, USA, pp 631–640 Liu Y, Jin R, Yang L (2006) Semi-supervised multi-label learning by constrained non-negative matrix factorization. In: AAAI, Orlando, FL, USA Lu Q, Getoor L (2003) Link-based classification. In: ICML: New York, NY, USA Luxburg Uv (2007) A tutorial on spectral clustering. Stat Comput 17(4): 395–416 Macskassy SA, Provost F (2003) A simple relational classifier. In: Proceedings of the multi-relational data mining workshop (MRDM) at the ninth ACM SIGKDD international conference on knowledge discovery and data mining, ACM Press, New York, NY, USA Macskassy SA, Provost F (2007) Classification in networked data: a toolkit and a univariate case study. J Mach Learn Res 8: 935–983 McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27: 415–444 Menon AK, Elkan C (2010) Predicting labels for dyadic data. Data Min Knowl Discov 21(2): 327–343 Neville J, Jensen D (2005) Leveraging relational autocorrelation with latent group models. In: MRDM ’05: proceedings of the 4th international workshop on Multi-relational mining. ACM, New York, NY, USA, pp 49–55 Newman M (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E Stat Nonlin Soft Matter Phys 74(3) Newman M (2006) Modularity and community structure in networks. PNAS 103(23): 8577–8582 Nowicki K, Snijders TAB (2001) Estimation and prediction for stochastic blockstructures. J Am Stat Assoc 96(455): 1077–1087 Sarkar P, Moore AW (2005) Dynamic social network analysis using latent space models. SIGKDD Explor Newsl 7(2): 31–40 Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29(3): 93 Shi J, Malik J (1997) Normalized cuts and image segmentation. In: CVPR ’97: proceedings of the 1997 conference on computer vision and pattern recognition (CVPR ’97). IEEE Computer Society, Washington, DC, USA, pp 731 Tang L, Liu H (2009a) Relational learning via latent social dimensions. In: KDD ’09: proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, USA, pp 817–826 Tang L, Liu H (2009b) Scalable learning of collective behavior based on sparse social dimensions. In: CIKM ’09: proceeding of the 18th ACM conference on Information and knowledge management. ACM, New York, NY, USA, pp 1107–1116 Tang L, Liu H (1996) Community detection and mining in social media. Synthesis lectures on data mining and knowledge discovery. Morgan and Claypool Publishers, USA Tang L, Rajan S, Narayanan VK (2009) Large scale multi-label classification via metalabeler. In: WWW ’09: proceedings of the 18th international conference on world wide web. New York, NY, USA, pp 211–220 Taskar B, Abbeel P, Koller D (2002) Discriminative probabilistic models for relational data. In: UAI, Edmonton, Canada, pp 485–492 Taskar B, Segal E, Koller D (2001) Probabilistic classification and clustering in relational data. In: IJCAI’01: proceedings of the 17th international joint conference on artificial intelligence. Morgan Kaufmann Publishers Inc, San Francisco, CA, USA, pp 870–876 Thelwall M (2009) Homophily in myspace. J Am Soc Inf Sci Technol 60(2): 219–231 Travers J, Milgram S (1969) An experimental study of the small world problem. Sociometry 32(4): 425–443 Tsoumakas G, Katakis I (2007) Multi label classification: an overview. Int J Data Wareh Min 3(3): 1–13 Tsuda K, Noble WS (2004) Learning kernels from biological networks by maximizing entropy. Bioinformatics 20: 326–333 Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge Wellman B (1926) The school child’s choice of companions. J Edu Res 14: 126–132 Xu Z, Tresp V, Yu S, Yu K (2008) Nonparametric relational learning for social network analysis. In: KDD’2008 workshop on social network mining and analysis, Las Vegas, NV, USA Zha H, He X, Ding CHQ, Gu M, Simon HD. (2001) Spectral relaxation for k-means clustering. In: NIPS, Vancouver, Canada, pp 1057–1064 Zhou D, Bousquet O, Lal T, Weston J, Scholkopf B (2004) Learning with local and global consistency. In: Advances in neural information processing systems 16: proceedings of the 2003 conference. Bradford Book, Cambridge, pp 321 Zhu X (2006) Semi-supervised learning literature survey. MIT Press, Cambridge, USA Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using gaussian fields and harmonic functions. In: ICML, New York, NY, USA