Proceedings of The 5th Indonesian International Conference on Innovation, Entrepreneurship, and Small Business , Jun 24, 2013
In Social Computing, Social Network Analysis (SNA) provides models and techniques for analysing s... more In Social Computing, Social Network Analysis (SNA) provides models and techniques for analysing social and economic network based on graph theory. SNA can help us to understand the real-world network application such as knowledge management, market segmentation, viral marketing, customer behavior, competitive advantage and many other applications. The ability to quantify complex network can greatly give an advantage for decision support. There are three approaches in current SNA study: Graph Representation, Content Mining, and Semantic Analysis. In this paper, we focus on graph representation approach which has been used for analyzing social network topology, structural modeling, tie-strength, community detection, group cohesion, visualization, and metrics computations. Many research contribute to the development of SNA, they are based from various idea and sometimes is difficult to track the development of this field. This paper provides SNA taxonomy based on its graph representation
Bookmarks Related papers MentionsView impact
Uploads
Papers by Budi Rahardjo
Social network analysis provides several metrics, which was built with no scalability in minds, thus it is computationally exhaustive. Some metrics such as centrality and community detections has exponential time and space complexity. With the availability of cheap but large-scale data, our challenge is how to measure social interactions based on those large-scale data. In this paper, we present our approach to reduce the computational complexity of social network analysis metrics based on graph compression method to solve real world brand awareness effort.