Framework for Enhancement Performance of Heterogeneous System via Social Network Analysis

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 ？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