Browse > Article
http://dx.doi.org/10.3837/tiis.2018.08.006

MFSC: Mean-Field-Theory and Spreading-Coefficient Based Degree Distribution Analysis in Social Network  

Lin, Chongze (Zhejiang Economic Information Center)
Zheng, Yi (Zhejiang Economic Information Center)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.8, 2018 , pp. 3630-3656 More about this Journal
Abstract
Degree distribution can provide basic information for structural characteristics and internal relationship in social network. It is a critical procedure for social network topology analysis. In this paper, based on the mean-field theory, we study a special type of social network with exponential distribution of time intervals. First of all, in order to improve the accuracy of analysis, we propose a spreading coefficient algorithm based on intimate relationship, which determines the number of the joined members through the intimacy among members. Then, simulation show that the degree distribution of follows the power-law distribution and has small-world characteristics. Finally, we compare the performance of our algorithm with the existing algorithms, and find that our algorithm improves the accuracy of degree distribution as well as reducing the time complexity significantly, which can complete 29.04% higher precision and 40.94% lower implementation time.
Keywords
Social network; degree distribution; mean-field theory; spreading coefficient;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. Z. Ji, X. J. Song, C. N. Liu, and X. Z. Zhang, "Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks," Physical A: Statistical Mechanics and its Applications, vol. 392, no. 15, pp. 3260-3272, August 2013.   DOI
2 Golder S., Wilkinson D., and Huberman B., "Rhythms of Social Interaction: Messaging Within a Massive Online Network," in Proc. of Proceedings of the 3rd International Conference on Communities and Technologies, pp. 1-16, 2007.
3 Meyers L. A., Pourbohloul, Newman M. E. J. and et al, "Network theory and SARS: predicting outbreak diversity," Journal of Theoretical Biology, vol. 232, no. 1, pp. 71-81, January 2005.   DOI
4 G. M. Park, S. H. Kim, and H. G. Cho, "Structural Analysis on Social Network Constructed from Characters in Literature Texts," Journal of Computers, vol. 8, no. 9, pp. 2442-2447, September 2013.
5 X. W. Yang, Y. Guo, and Y. Liu, "Bayesian-Inference-Based Recommendation in Online Social Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 4, pp. 642-651, July 2013.   DOI
6 Z. Wang, L. F. Sun, W. W. Zhu and et al, "Joint Social and Content Recommendation for User-Generated Videos in Online Social Network," IEEE Transactions on Multimedia, vol. 15, no. 3, pp. 698-709, April 2013.   DOI
7 Keeling M. J. and Eames K. T. D., "Networks and epidemic models," Journal of the royal society interface, vol. 2, no. 4, pp. 295-307, June 2005.   DOI
8 J. Jiang, C. Wilson, X. Wang and et al, "Understanding Latent Interactions in Online Social Networks," ACM Transactions on the Web, vol. 7, no. 4, pp. 369-382, October 2013.
9 Albert R. and Barabasi A. L., "Statistical mechanics of complex network," Review of Modern Physics, vol. 74, no. 1, pp. 47-97, June 2002.   DOI
10 L. Y. Luu and T. Zhou, "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, vol. 390, no. 6, pp. 1150-1170, March 2011.   DOI
11 Krapivsky P. L. and Redner S., "Distinct degrees and their distribution in complex networks," Journal of Statistical Mechanics: Theory and Experiment, vol. 2013, no. 6, pp. 1-12, April 2013.
12 Newman M. E. J., "The structure and function of complex network," SIAM Rev, vol. 45, no. 2, pp. 167-256, August 2003.   DOI
13 Mislove A., Marcon M., Gummadi K. P. and et al, "Measurement and Analysis of Online Social Networks," in Proc. of Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29-42, October 24-26, 2007.
14 R. Albert and Barabasi A. L., "Topology of Evolving Networks: Local Events and Universality," Phys.Rev.Lett, vol. 85, no. 24, pp. 5234-5237, May 2000.   DOI
15 A. Dwivedi and X. H. Yu, "A Maximum-Flow-Based Complex Network Approach for Power System Vulnerability Analysis," IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 81-88, February 2013.   DOI
16 Mahdi Jalili, "Social power and opinion formation in complex networks," Physica A: Statistical Mechanics and its Applications, vol. 392, no. 4, pp. 959-966, February 2013.   DOI
17 Barabasi A. L., R. Albert, and H. Jeong, "Mean-field theory for scale-free random networks," Physical A, vol. 272, no. 1-2, pp. 173-187, July 1999.   DOI
18 Z. Li, C. Wang, S. Q. Yang and et al, "LASS: Local-Activity and Social-Similarity Based Data Forwarding in Mobile Social Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 1, pp. 1-15, January 2015.   DOI
19 Darko Obradovic, Stephan Baumann, and Andreas Dengel, "A social network analysis and mining methodology for the monitoring of specific domains in the blogosphere," Social Network Analysis and Mining, vol. 3, no. 2, pp. 221-232, June 2013.   DOI
20 M. Boguna, R. Pastor-Satorras, A. Diaz-Guilera and A. Arenas, "Models of social networks based on social distance attachment," Physical Review E, vol. 70, no. 056122, pp. 1-8, November 2004.
21 A. Lancichinetti, S. Fortunato and J. Kerteesz, "Detecting the over-lapping and hierarchical community structure in complex networks," New Journal of Physics, vol. 11, pp. 033015, March 2009.   DOI
22 G. Song, Y. Li, X. Chen, X. He, and J. Tang, "Influential Node Tracking on Dynamic Social Network: An Interchange Greedy Approach," IEEE Trans. Knowl. Data Eng., vol. 29, no. 2, pp. 359-372, 2017.   DOI
23 Y. Zheng, D. F. Zhang, and K. Xie, "An Intimacy-based Algorithm for Social Network Community Detection," in Proc. of The 15th International Conference on Algorithms and Architectures for Parallel Processing, pp. 764-776, August 2015.
24 X. Yang, H. Steck, and Y. Liu, "Circle-based recommendation in online social networks," in Proc. of KDD, pp. 1267-1275, 2012.
25 G. Zhao, X. Qian, and X. Xie, "User-Service Rating Prediction by Exploring Social Users' Rating Behaviors," IEEE Trans. Multimedia, vol. 18, no. 3, pp. 496-506, 2016.   DOI
26 G. Zhao, X. Qian, X. Lei, and T. Mei, "Service Quality Evaluation by Exploring Social Users' Contextual Information," IEEE Trans. Knowl. Data Eng., vol. 28, no. 12, pp. 3382-3394, 2016.   DOI
27 X. Qian, H. Feng, G. Zhao, and T. Mei, "Personalized Recommendation Combining User Interest and Social Circle," IEEE Trans. Knowl. Data Eng., vol. 26, no. 7, pp. 1763-1777, 2014.   DOI
28 W. H. He, L. Feng, L. Y. Li, and C. Q. Xu, "Time evolution of the degree distribution of model A of random attachment growing networks," Physica A, vol. 384, no. 2, pp. 663-666, 2007.   DOI
29 S. P. Borgatti, M. G. Everett, and L. C. Freeman, "UCINET," Encyclopedia of Social Network Analysis & Mining, vol. 15, no. 7, pp. 536-544, 2014.
30 Amaral L. A. N. and Ottino J.M., "Complex networks: Augmenting the framework for the study of complex systems," The European physical journal B, vol. 38, no. 2, pp. 147-162, March 2004.   DOI
31 J. Leskovec, "SNAP: Stanford Network Analysis Project," February 2014.
32 Watts D. J. and Strongatz S. H., "Collective dynamics of 'small-world' networks," Nature, vol. 393, no. 6684, pp. 440-442, April 1998.   DOI
33 M. Muller and D. W. Sun, "Directing the Self-Assembly of Block Copolymers into A Metastable Complex Network Phase via A Deep and Rapid Quench," Physical Review Letters, vol. 111, no. 26, pp. 268101, December 2013.   DOI
34 Y. Li and C. Zheng, "The complex network synchronization via chaos control nodes," Journal of Applied Mathematics, vol. 2013, no. 2013, pp. 1-11, 2013.
35 Barabasi A. L. and Albert R., "Emergence of Scaling in Random Networks," Science, vol. 286, no. 5439, pp. 509-512, October 1999.   DOI
36 J. F. Box, "Guinness, Gosset, Fisher, and Small Samples," Statistical Science, vol. 2, no. 1, pp. 45-52, 1987.   DOI
37 A. Lancichinetti, S. Fortunato, and J. Kertesz, "Detecting the overlapping and hierarchical community structure in complex networks," New Journal of Physics, vol. 11, no. 3, pp. 1-20, 2009.