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[학술저널]

  • 학술저널

Yosini Deliana(Universitas Padjadjaran) Irlan Adiyatma Rum(Universitas Padjadjaran)

DOI : 10.7232/iems.2019.18.3.474

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초록

The network literature has shown that the great interest is to identify communities. There are many approaches for finding such partitions and one of them is by using modularity-maximizing graph. In this paper, graph application is used to determine cross-sector communities in the economy, especially related with agricultural sector. From I/O analysis, we derive intersectoral linkages and determine the leading sectors in the economy. Then, we use a particular natural approach called Leuvain modularity maximization to compute the modularity clustering across sectors. This paper reveals conditions for or properties of the maximum modularity of an intersectoral network. The number of clusters vary when the intersectoral network is changed. This paper shows that this approach creates interesting community structures for agricultural sector. Finally, we highlight the performance and quality of our approach versus standard I/O analysis.

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ABSTRACT
1. INTRODUCTION
2. DATA AND METHODS
3. RESULTS
4. CONCLUSION
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