Abstract
Recently, Social Network Service (SNS) users are rapidly increasing, and Social Network Analysis (SNA) methods are used to analyze the structure of user relationship or messages in many fields. However, the SNA methods based on the shortest distance among nodes is time-consuming in measuring computation time. In order to solve this problem, we present a heuristic method for the shortest path search using SNS user graphs. Our proposed method consists of three steps. First, it sets a start node and a goal node in the Social Network (SN), which is represented by trees. Second, the goal node sets a temporary node starting from a skewed tree, if there is a goal node on a leaf node of the skewed tree. Finally, the betweenness and closeness centralities are computed with the heuristic shortest path search. For verification of the proposed method, we demonstrate an experimental analysis of betweenness centrality and closeness centrality, with 164,910 real data in an SNS. In the experimental results, the method shows that the computation time of betweenness centrality and closeness centrality is faster than the traditional method. This heuristic method can be used to analyze social phenomena and trends in many fields.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Ellison NB, Steinfield C, Lampe C (2007) The benefits of facebook “friends:” social capital and college students’ use of online social network sites. J Comput Mediated Commun 12(4):1143–1168
Kwak H, Lee C, Park HS, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web, North Carolina, USA, ACM, pp 591–600
Sohn JS, Chung IJ (2013) Dynamic FOAF management method for social network in the social web environment. J Supercomput
Otte E, Rousseau R (2002) Social network analysis: a powerful strategy, also for the information sciences. J Inf Sci 28(6):441–453
Huh MH, Lee YG (2011) Applying Monte-Carlo method in social network analysis. Appl Stat Res 24(2):401–409
Cho ID, Kim NK (2011) Recommending core and connecting keywords of research area using social network and data mining techniques. J Intell Inf Syst 17(1):127–138
Kim HK, Choi IY, Ha KM, Kim JK (2010) Development of user based recommender system using social network for u-healthcare. J Intell Inf Syst 16(3):181–199
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):26–113
Hummon NP, Doreian P (1990) Computational methods for social network analysis. Soc Netw 12(4):273–288
Shen Y, Pei WJ, Wang K, Wang SP (2009) A self-organizing shortest path finding strategy on complex networks. Chin Phys B 18(9):3783
Nan D, Wu B, Pei X, Wang B, Xu L (2007) Community detection in large-scale social networks. In: Proceedings of joint 9th WEBKDD and 1st SNA-KDD
Ahmet ES, Kamer K, Erik S, Umit VC (2013) Incremental algorithms for network management and analysis based on closeness centrality. In: Proceedings of arXiv:1303.0422v1
Heer J, Boyd D (2005) Vizster: visualizing online social networks. Information visualization. In: Proceedings of IEEE symposium on INFOVIS
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Sohn, J., Kang, D., Park, H., Joo, BG., Chung, IJ. (2014). An Improved Social Network Analysis Method for Social Networks. In: Huang, YM., Chao, HC., Deng, DJ., Park, J. (eds) Advanced Technologies, Embedded and Multimedia for Human-centric Computing. Lecture Notes in Electrical Engineering, vol 260. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7262-5_13
Download citation
DOI: https://doi.org/10.1007/978-94-007-7262-5_13
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-7261-8
Online ISBN: 978-94-007-7262-5
eBook Packages: EngineeringEngineering (R0)