Building Complex Network Similar to Facebook

Dong Wei Guo; Xiang Yan Meng; Cai Fang Hou

doi:10.4028/www.scientific.net/AMM.513-517.909

Paper Titles

Realization of Primitive Trace in TD-LTE RF Conformance Testing Instrument
p.893

The RRM Conformance Test of Measurement Process in TD-LTE System Based on TTCN-3
p.897

Design of TD-LTE PHY and MAC Layer in the Interactive System Primitives
p.901

The Development of RRM Conformance Test in TD-LTE Idle_State
p.905

Building Complex Network Similar to Facebook
p.909

Performance Analysis of One AP with 802.11g WLAN Network
p.914

Large Capacity Cache Design Based on Emerging Non-Volatile Memory
p.918

New Delay-Dependent Stability Criteria for Recurrent Neural Networks with Time-Varying Delays
p.922

Extending Linear Temporal Logic with Clocks to Object-Z
p.927

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517Building Complex Network Similar to Facebook

Building Complex Network Similar to Facebook

Abstract:

Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

909-913

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.909

Citation:

Cite this paper

Online since:

February 2014

Authors:

Dong Wei Guo*, Xiang Yan Meng, Cai Fang Hou

Keywords:

Average Path Length, Ba Scale-Free Network, Clustering Coefficient, Community Structure, Degree Distribution, Facebook

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] A. -L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286: 509, (1999).

Google Scholar

[2] Holme P., Kim B.J. Growing scale-free networks with tunable clustering[J]. Physical Review E, 2002 65(2): 026107.

Google Scholar

[3] Boguna M., et al. Models of social networks based on social distance attachment[J]. Physical Review E, 2004, 70(3): 036108.

Google Scholar

[4] M. Gjoka; M. Kurant; C.T. Butts; A. Markopoulou. Walking in Facebook: A Case Study of Unbiased Sampling of OSNs. INFOCOM, 2010 Proceedings IEEE[C], 2010, PP: 1-9.

DOI: 10.1109/infcom.2010.5462078

Google Scholar

[5] M.E.J. Newman, Clustering and preferential attachment in growing networks，Phys. Rev. E 64 016131(2001).

Google Scholar

[6] Newman M.E.J., Girvan M. Finding and evaluating communitity structure in Networks[J]. Physical Review E, 2004, 69(2): 026113-65.

Google Scholar

[7] Guo Dongwei, Wu Yunna and Meng Xiangyan, Analysis and Contrast the Statistic Characteristics of Real Data on Facebook，ICCCI 2012, I1009.

Google Scholar

[8] V. Colizza, A. Flammini, M.A. Serrano and A. Vespignani, Detecting rich-club ordering in complex networks, Nature Phys. 2 110 (2006).

DOI: 10.1038/nphys209

Google Scholar

[9] Fortunato S. Community detection in graphs[J]. Physics Reports, 2010, 486(35): 75-174.

Google Scholar