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

DOI

10.2991/iccst-15.2015.26How to use a DOI?

Keywords

Brand awareness, computational complexity, graph compression, large-scale data, social network, centrality

Abstract

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Andry Alamsyah
AU  - Yahya Peranginangin
AU  - Budi Rahardjo
AU  - Intan Muchtadi-Alamsyah
AU  - Kuspriyanto
PY  - 2015/01
DA  - 2015/01
TI  - Reducing Computational Complexity of Network Analysis using Graph Compression Method for Brand Awareness Effort
BT  - Proceedings of the 3rd International Conference on Computation for Science and Technology
PB  - Atlantis Press
SP  - 135
EP  - 140
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccst-15.2015.26
DO  - 10.2991/iccst-15.2015.26
ID  - Alamsyah2015/01
ER  -