Sampling connected induced subgraphs uniformly at random

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

Volumn 7338 LNCS, Issue , 2012, Pages 195-212

  Lu, Xuesong ^a   Bressan, Stéphane ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING TASKS; INDUCED SUBGRAPHS; LARGE GRAPHS; MARKOV CHAIN MONTE CARLO; MODERN APPLICATIONS; NOVEL ALGORITHM; RANDOM SAMPLING; RANDOM WALK; REPRESENTATIVE SAMPLE; SAMPLE SIZES; SAMPLING TECHNIQUE; SCALABILITY ISSUE; SUBGRAPHS; THREE DIFFERENT TECHNIQUES;

ARTIFICIAL INTELLIGENCE;

ALGORITHMS;

EID: 84863438815     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31235-9_13     Document Type: Conference Paper

References (21)

1. Stanford Network Analysis Project, http://snap.stanford.edu/index.html
2. Ahn, Y.-Y., Han, S., Kwak, H., Moon, S.B., Jeong, H.: Analysis of topological characteristics of huge online social networking services. In: WWW, pp. 835-844 (2007)
3. Batagelj, V., Brandes, U.: Efficient generation of large random networks. Physical Review E 71 (2005)
4. Cowles, M.K., Carlin, B.P.: Markov chain monte carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association 91, 883-904 (1996)
5. Geweke, J.: Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In: BAYESIAN STATISTICS, pp. 169-193. University Press (1992)
6. Gilks, W., Spiegelhalter, D.: Markov chain Monte Carlo in practice. Chapman &Hall/CRC (1996)
7. Hastings, W.K.: Monte carlo sampling methods using markov chains and their applications. Biometrika 57(1), 97-109 (1970)
8. Henzinger, M.R., Heydon, A., Mitzenmacher, M., Najork, M.: On near-uniform url sampling. Computer Networks 33(1-6), 295-308 (2000)
9. Hübler, C., Kriegel, H.-P., Borgwardt, K.M., Ghahramani, Z.: Metropolis algorithms for representative subgraph sampling. In: ICDM, Pisa, Italy, pp. 283-292 (December 2008)
10. Kashtan, N., Itzkovitz, S., Milo, R., Alon, U.: Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20(11), 1746-1758 (2004)
11. Kwak, H., Lee, C., Park, H., Moon, S.B.: What is twitter, a social network or a news media? In: WWW, pp. 591-600 (2010)
12. Leon-Garcia, A.: Probability, Statistics, and Random Processes for Electrical Engineering. Prentice Hall (2008)
13. Leskovec, J., Faloutsos, C.: Sampling from large graphs. In: KDD, Philadelphia, Pennsylvania, USA, pp. 631-636 (August 2006)
14. Maiya, A.S., Berger-Wolf, T.Y.: Sampling community structure. In: WWW, Raleigh, North Carolina, USA, pp. 701-710 (April 2010)
15. Milo, R., Kashtan, N., Itzkovitz, S., Newman, M.E.J., Alon, U.: On the uniform generation of random graphs with prescribed degree sequences (May 2004)
16. Nobari, S., Lu, X., Karras, P., Bressan, S.: Fast random graph generation. In: EDBT, pp. 331-342 (2011)
17. Przulj, N., Corneil, D.G., Jurisica, I.: Efficient estimation of graphlet frequency distributions in protein-protein interaction networks. Bioinformatics 22(8), 974-980 (2006)
18. Ribeiro, B., Towsley, D.: Estimating and sampling graphs with multidimensional random walks. In: IMC, Melbourne, Australia (November 2010)
19. Vázquez, A., Oliveira, J., Barabäsi, A.: Inhomogeneous evolution of subgraphs and cycles in complex networks. Physical Review E (2005)
20. Viger, F., Latapy, M.: Efficient and Simple Generation of Random Simple Connected Graphs with Prescribed Degree Sequence. In: Wang, L. (ed.) COCOON 2005. LNCS, vol. 3595, pp. 440-449. Springer, Heidelberg (2005) (Pubitemid 41450222)
21. Vitter, J.S.: Random sampling with a reservoir. ACM Trans. Math. Softw. 11(1), 37-57 (1985)