Framework for Enhancement Performance of Heterogeneous System via Social Network Analysis
Keywords:Vertex, edges, Semantic network, clustering, blog, attribute, machine Learning
In recent decades, social network analysis arise as a powerful tool for describing social competitive systems. Vertices, or entities represent states or nodes or agents, while edges represent relations between different nodes. A graph theory is a suitable way for simulating complicated systems. This paper presents framework and architecture to improve system performance using social network analysis. System crawling then web blog analysis is a methodology to clustering and exploring community nodes. Graph and semantic network used for simulate agents and edges. The proposed architecture involves two consecutive modules affecting system performance. Clustering is a first module which simulate vertices and it's edges with user supervised for filtering nodes and attribute , while supervised and Â machine learning module stimulate the agents for optimal path and performance under complicated environment circumstances. Data prepared from social networks, relations extracted, and crawling with breadth first search with some small graphs as a base are maintained. The proposed framework affect the performance of learning in university as a sample of community through analyzing and maintaining relations and features enhancement which are achieved via supervised machine learning. The proposed system can be implemented to a various social institutes or organizations, using its webs or blogs with different queries from interviews and emails to enhance its performance.
Gruzd Anatoliy. (2008) The Analysis of Online Communities using Interactive Content-based Social Networks , Graduate School of Library and Information Science, University of Illinois at Urbana- Champaign.
Hanneman, Robert A. (2005) Introduction to Social Network Methods, Department of Sociology, University of California.
Chin, Alvin, Chignell Mark (2005) Finding Evidence of Community From Blogging Co- Citations :A Social Netwok Analytic Approach , University of Toronto.
Chin, Alvin, Chignell, Mark (2007) Identifying Communities in blogs : roles of Social Network Analysis and Survey Instruments , Int. J. Web Based Communities, Vol. 3, No. 3.
Bohn, Angela, Feinerer, Ingo, Hornik , Kurt, and Patrick Mair (June 2001) Content Based Social Network Analysis for Maling Lists, The R Journal Vol. 3/1.
Waldstrorm,Christian (August 2003) Understanding Intra-Organizational Relations Through Social Network Analysis , Department of Organization and Management " Aarhus School of Business.
Mislove, Alan, Marcon, Massimiliano, Gummadi, Krishna P, Druschel, Peter, and Bhattacharjee, Bobby (2008) Measurement and Analysis of Social Network Analysis.
Hoppe, Bruce, Reinelt, Claire (2010) Social Network Analysis and the Evaluation of Leadership Networks, journal homepage: www.el sevier.com/ locate/ leaqua.
Ferrara Emilio (2012) Mining and Analysis of Online Social Networks, A thesis submitted for the degree of Philosophi_Doctor (PhD) in Mathematics 2012 February. Department of Mathematics ,University of Messina.
Cai, Deng, Zheng Shao, He, Xiaofei,Han, Jiawei (2005) Mining Hidden Community in heterogeneous Social Networks , Department of Computer Science, University of Chicago.
Golbeck, Jennifer (2006) The Dynamics of Web-Based Social Network : Membership, Relationships, and Change ", University of Myer land.
Shi, Xiaodong (2006) Social Network Analysis of Web Search Engine Query Logs , School of Information , University of Michigan.
Kazienko, PrzemysÅ‚aw, Michalski , RadosÅ‚aw , Palus , Sebastian (June 29-July 1,2011) Social Network Analysis as a Tool for Improving Enterprize Architecture, Proceedings of the 5th International KES Symposium on Agents and Multi-agent Systems, KES-AMSTA, Manchester .
Lichman, M (2013) UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
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