Attributed graph models: Modeling network structure with correlated attributes

WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web

Volumn , Issue , 2014, Pages 831-841

  Pfeiffer, Joseph J ^a   Moreno, Sebastian ^a   La Fond, Timothy ^a   Neville, Jennifer ^a   Gallagher, Brian ^b  

^a PURDUE UNIVERSITY   (United States)

^b LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

Author keywords

Attributed graph models; Network analysis; Network modeling

Indexed keywords

ELECTRIC NETWORK ANALYSIS; PROBABILITY; SOCIAL NETWORKING (ONLINE);

ATTRIBUTED GRAPHS; COMMUNITY STRUCTURES; NETWORK MODELING; ON-LINE SOCIAL NETWORKS; POWER LAW DEGREE DISTRIBUTION; STATISTICAL RELATIONAL LEARNING; STRUCTURAL CHARACTERISTICS; USER CHARACTERISTICS;

GRAPH THEORY;

EID: 84909606699     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2566486.2567993     Document Type: Conference Paper

References (24)

1. E. M. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing. Mixed membership stochastic blockmodels. J. Mach. Learn. Res., 9:1981-2014, June 2008.
2. F. Chung and L. Lu. The average distances in random graphs with given expected degrees. Internet Mathematics, 1, 2002.
3. P. Erdos and A. Renyi. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci, 5:17-61, 1960.
4. L. Getoor. Learning Statistical Models from Relational Data. PhD thesis, Stanford, 2001.
5. M. S. Handcock. Assessing degeneracy in statistical models of social networks.
6. P. D. Hoff, A. E. Raftery, and M. S. Handcock. Latent space approaches to social network analysis. Journal of the American Statistical Association, 97:1090+, December 2002.
7. M. Kim and J. Leskovec. Multiplicative attribute graph model of real-world networks. Int Mathematics, 2012.
8. T. G. Kolda, A. Pinar, T. Plantenga, and C. Seshadhri. A scalable generative graph model with community structure. arXiv:1302.6636, February 2013. revised March 2013.
9. D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press, 2009.
10. T. La Fond and J. Neville. Randomization tests for distinguishing social inuence and homophily effects. WWW '10, 2010.
11. J. Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos, and Z. Ghahramani. Kronecker graphs: An approach to modeling networks. J. Mach. Learn. Res., 2010.
12. J. Leskovec and E. Horvitz. Planetary-scale views on a large instant-messaging network. Proceedings of the 17th international conference on World Wide Web, 2008.
13. F. Liang, C. Liu, and R. J. Carrol. Advanced Markov chain Monte Carlo methods: learning from past samples. Wiley Series in Computational Statistics. Wiley, New York, NY, 2010.
14. A. McCallum, K. Nigam, J. Rennie, and K. Seymore. Automating the construction of internet portals with machine learning. Information Retrieval, 3(2):127-163, 2000.
15. M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual review of sociology, pages 415-444, 2001.
16. S. Moreno, S. Kirshner, J. Neville, and S. Vishwanathan. Tied kronecker product graph models to capture variance in network populations. In Allerton'10, pages 17-61, 2010.
17. J. Neville, O. Şimşek, D. Jensen, J. Komoroske, K. Palmer, and H. Goldberg. Using relational knowledge discovery to prevent securities fraud. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, KDD '05, pages 449-458, New York, NY, USA, 2005. ACM.
18. J. J. Pfeiffer III, T. La Fond, S. Moreno, and J. Neville. Fast generation of large scale social networks while incorporating transitive closures. In (SocialCom), 2012.
19. A. Pinar, C. Seshadhri, and T. G. Kolda. The similarity between stochastic kronecker and chung-lu graph models. CoRR, abs/1110.4925, 2011.
20. G. Robins, P. Pattison, Y. Kalish, and D. Lusher. An introduction to exponential random graph (p∗) models for social networks. Social Networks, May 2007.
21. C. R. Shalizi and A. Rinaldo. Consistency under sampling of exponential random graph models. The Annals of Statistics, 41(2):508-535, 04 2013.
22. C. R. Shalizi and A. C. Thomas. Homophily and contagion are generically confounded in observational social network studies. Sociological Methods and Research, 40.2:211-239, 2011.
23. D. Stirling. Mathematical Analysis: A Fundamental and Straightforward Approach. MATHEMATICS AND ITS APPLICATIONS. Prentice Hall, 1987.
24. B. Taskar. Learning structured prediction models: a large margin appr. PhD thesis, Stanford, 2004.