Proceedings of the 3rd International Conference on Computation for Science and Technology

Reducing Computational Complexity of Network Analysis using Graph Compression Method for Brand Awareness Effort

Andry Alamsyah, Yahya Peranginangin, Budi Rahardjo, Intan Muchtadi-Alamsyah, Kuspriyanto
Corresponding author
Brand awareness, computational complexity, graph compression, large-scale data, social network, centrality
Online social media provides platform for social interactions. This platform produce large-scale data generated mostly from online conversations. Network analysis can help us to mine knowledge and pattern from the relationship between actors inside the network. This approach has been crucial in supporting prediction and decision-making process. In marketing context such as branding effort, using large-scale conversation data is cheaper, faster and reliable comparing mainstream approaches such as questionnaire and sampling. 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.
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