Sampling from complex networks using distributed learning automata

Physica A: Statistical Mechanics and its Applications

Volumn 396, Issue , 2014, Pages 224-234

  Rezvanian, Alireza ^a   Rahmati, Mohammad ^a   Meybodi, Mohammad Reza ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Complex networks; Distributed learning automata; Network sampling; Social networks

Indexed keywords

AUTOMATA THEORY; INFORMATION SERVICES; LEARNING ALGORITHMS; ROBOTS; SOCIAL NETWORKING (ONLINE);

BIOLOGICAL NETWORKS; DISTRIBUTED LEARNING AUTOMATON; ECOLOGICAL NETWORKS; INFORMATION NETWORKS; NETWORK SAMPLINGS; ON-LINE SOCIAL NETWORKS; SAMPLING ALGORITHM; TECHNOLOGICAL NETWORKS;

COMPLEX NETWORKS;

EID: 84890568327     PISSN: 03784371     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.physa.2013.11.015     Document Type: Article

References (55)

1. D.J. Watts, and S.H. Strogatz Collective dynamics of "small- world" networks Nature 393 1998 440 442
2. A.L. Barabási, and R. Albert Emergence of scaling in random networks Science 286 1999 509 512
3. S. Hu, H. Yang, B. Cai, and C. Yang Research on spatial economic structure for different economic sectors from a perspective of a complex network Physica A 392 2013 3682 3697
4. J. Tang, Y. Wang, and F. Liu Characterizing traffic time series based on complex network theory Physica A 392 2013 4192 4201
5. G. Zhang, Z. Li, B. Zhang, and W.A. Halang Understanding the cascading failures in Indian power grids with complex networks theory Physica A 392 2013 3273 3280
6. I. Sorkhoh, K.A. Mahdi, and M. Safar Estimation algorithm for counting periodic orbits in complex social networks Inf. Syst. Front. 15 2013 193 202
7. J. Ma, D. Zeng, and H. Zhao Modeling the growth of complex software function dependency networks Inf. Syst. Front. 14 2012 301 315
8. L.F. Costa, F.A. Rodrigues, G. Travieso, and P.R.V. Boas Characterization of complex networks: a survey of measurements Adv. Phys. 56 2007 167 242
9. M. Papagelis, G. Das, and N. Koudas Sampling Online social networks IEEE Trans. Knowl. Data Eng. 25 2013 662 676
10. M. Kurant, A. Markopoulou, and P. Thiran Towards unbiased BFS sampling IEEE J. Sel. Areas Commun. 29 2011 1799 1809
11. M. Gjoka, C.T. Butts, M. Kurant, and A. Markopoulou Multigraph sampling of online social networks IEEE J. Sel. Areas Commun. 29 2011 1893 1905
12. F. Murai, B. Ribeiro, D. Towsley, and P. Wang On set size distribution estimation and the characterization of large networks via sampling IEEE J. Sel. Areas Commun. 31 2013 1017 1025
13. E. Volz, and D.D. Heckathorn Probability based estimation theory for respondent driven sampling J. Off. Stat.-Stockholm 24 2008 79
14. M. Huisman Imputation of missing network data: some simple procedures J. Soc. Struct. 10 2009 1 29
15. J. Leskovec, C. Faloutsos, Sampling from large graphs, in: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, pp. 631-636.
16. M. Gjoka, M. Kurant, C.T. Butts, A. Markopoulou, Walking in facebook: a case study of unbiased sampling of OSNs, in: 2010 Proceedings IEEE INFOCOM, 2010, pp. 1-9.
17. E. Gilbert, K. Karahalios, Predicting tie strength with social media, in: Proceedings of the 27th International Conference on Human Factors in Computing Systems, 2009, pp. 211-220.
18. The size of the World Wide Web (The Internet), 2013. Online: http://www.worldwidewebsize.com/.
19. Y. Jia, J. Hoberock, M. Garland, and J. Hart On the visualization of social and other scale-free networks IEEE Trans. Vis. Comput. Graphics 14 2008 1285 1292
20. S. Lafon, and A.B. Lee Diffusion maps and coarse-graining: a unified framework for dimensionality reduction, graph partitioning, and data set parameterization IEEE Trans. Pattern Anal. Mach. Intell. 28 2006 1393 1403
21. A. Zeng, and L. Lü Coarse graining for synchronization in directed networks Phys. Rev. E 83 2011 056123
22. S.N. Dorogovtsev, A. Goltsev, and J.F.F. Mendes K-core organization of complex networks Phys. Rev. Lett. 96 2006 40601
23. J.S. Kim, K.I. Goh, B. Kahng, and D. Kim Fractality and self-similarity in scale-free networks New J. Phys. 9 2007 177
24. S.H. Lee, P.J. Kim, and H. Jeong Statistical properties of sampled networks Phys. Rev. E 73 2006 016102
25. S. Even Graph Algorithms second ed. 2011 Cambridge University Press
26. M. Kurant, A. Markopoulou, P. Thiran, On the bias of BFS (Breadth First Search), in: 2010 22nd International Teletraffic Congress, ITC, 2010, pp. 1-8.
27. O. Frank Survey sampling in networks The SAGE Handbook of Social Network Analysis 2011 SAGE publications 370 388
28. S. Yoon, S. Lee, S.H. Yook, and Y. Kim Statistical properties of sampled networks by random walks Phys. Rev. E 75 2007 046114
29. C.H. Lee, X. Xu, D.Y. Eun, Beyond random walk and Metropolis-Hastings samplers: why you should not backtrack for unbiased graph sampling, in: Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, 2012, pp. 319-330.
30. M. Kurant, M. Gjoka, C.T. Butts, A. Markopoulou, Walking on a graph with a magnifying glass, in: Proceedings of ACM SIGMETRICS, 2011, pp. 1-12.
31. S. Goel, and M.J. Salganik Assessing respondent-driven sampling Proc. Natl. Acad. Sci. 107 2010 6743 6747
32. K.J. Gile, and M.S. Handcock Respondent-driven sampling: an assessment of current methodology Sociol. Methodol. 40 2010 285 327
33. M. Salehi, H.R. Rabiee, N. Nabavi, S. Pooya, Characterizing twitter with respondent-driven sampling, in: 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, DASC, 2011, pp. 1211-1217.
34. C. Cooper, T. Radzik, Y. Siantos, A fast algorithm to find all high degree vertices in power law graphs, in: Proceedings of the 21st International Conference Companion on World Wide Web, 2012, pp. 1007-1016.
35. B. Ribeiro, D. Towsley, Estimating and sampling graphs with multidimensional random walks, in: Proceedings of the 10th Annual Conference on Internet Measurement, 2010, pp. 390-403.
36. L. Jin, Y. Chen, P. Hui, C. Ding, T. Wang, A.V. Vasilakos, et al. Albatross sampling: robust and effective hybrid vertex sampling for social graphs, in: Proceedings of the 3rd ACM International Workshop on MobiArch, 2011, pp. 11-16.
37. A.S. Maiya, T.Y. Berger-Wolf, Sampling community structure, in: Proceedings of the 19th International Conference on World Wide Web, 2010, pp. 701-710.
38. J. Wang, Y. Guo, Unbiased sampling of bipartite graph, in: 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC, 2011, pp. 357-360.
39. B. Ribeiro, P. Wang, F. Murai, D. Towsley, Sampling directed graphs with random walks, in: Proceedings IEEE INFOCOM, 2012, pp. 1692-1700.
40. C.-L. Yang, P.-H. Kung, C.-A. Chen, S.-D. Lin, Semantically sampling in heterogeneous social networks, in: Proceedings of the 22nd International Conference on World Wide Web Companion, 2013, pp. 181-182.
41. M. Salehi, H.R. Rabiee, and A. Rajabi Sampling from complex networks with high community structures Chaos 22 2012 023126
42. K.S. Narendra, and M.A.L. Thathachar Learning Automata: An Introduction 1989 Prentice-Hall
43. M.A.L. Thathachar, and P.S. Sastry Networks of Learning Automata: Techniques for Online Stochastic Optimization 2004 Kluwer Academic Publishers
44. J. Akbari Torkestani, and M.R. Meybodi Finding minimum weight connected dominating set in stochastic graph based on learning automata Inform. Sci. 200 2012 57 77
45. A. Rezvanian, M.R. Meybodi, An adaptive mutation operator for artificial immune network using learning automata in dynamic environments, in: Proceedings of the 2010 Second World Congress on Nature and Biologically Inspired Computing, NaBIC, 2010: pp. 479-483.
46. A. Rezvanian, and M.R. Meybodi LACAIS: learning automata based cooperative artificial immune system for function optimization Contemporary Computing 2010 Springer Berlin, Heidelberg 64 75
47. A. Rezvanian, M.R. Meybodi, and T. Kim Tracking extrema in dynamic environments using a learning automata-based immune algorithm Grid and Distributed Computing, Control and Automation 2010 Springer Berlin, Heidelberg 216 225
48. J. Akbari Torkestani A highly reliable and parallelizable data distribution scheme for data grids Future Gener. Comput. Syst. 29 2013 509 519
49. F. Amiri, N. Yazdani, H. Faili, and A. Rezvanian A novel community detection algorithm for privacy preservation in social networks A. Abraham, Intelligent Informatics 2013 443 450
50. H. Beigy, and M.R. Meybodi Utilizing distributed learning automata to solve stochastic shortest path problems Int. J. Uncertain. Fuzz. 14 2006 591
51. J. Akbari Torkestani, and M.R. Meybodi Clustering the wireless Ad Hoc networks: a distributed learning automata approach J. Parallel Distrib. Comput. 70 2010 394 405
52. R. Forsati, and M.R. Meybodi Effective page recommendation algorithms based on distributed learning automata and weighted association rules Expert Syst. Appl. 37 2010 1316 1330
53. M. Soleimani-Pouri, A. Rezvanian, M.R. Meybodi, Solving maximum clique problem in stochastic graphs using learning automata, in: 2012 Fourth International Conference on Computational Aspects of Social Networks, CASoN, 2012, pp. 115-119.
54. M.L. Goldstein, S.A. Morris, and G.G. Yen Problems with fitting to the power-law distribution Eur. Phys. J. B 41 2004 255 258
55. Z. Bar-Yossef, and M. Gurevich Random sampling from a search engine's index J. ACM 55 2008 24:1 24:74