Publication:
Network mining: Applications to business data

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Date

2014-07

Authors

Çavdur, Fatih

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Kumara, Soundar R.

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Springer

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Abstract

This research addresses the problem of analyzing the temporal dynamics of business organizations. In particular, we concentrate on inferring the related businesses, i.e., are there groups of companies that are highly correlated through some measurement (metric)? We argue that business relationships derived from general literature (i.e., newspaper articles, news items etc.) may help us create a network of related companies (business networks). On the other hand, relative movement of stock prices can give us an indication of related companies (asset graphs). We also expect to see some relationships between these two kinds of networks. We adapt the asset graph construction approach from the literature for our asset graph implementations, and then, define our methodology for business network construction. Finally, an introduction to the exploration of some relationships between the asset graphs and business networks is presented.

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Keywords

Algorithms, Stock market, Business systems, Networks, Financial systems, Network mining, Algorithms, Networks (circuits), Finance, Business networks, Business organizations, Highly-correlated, Business relationships, Graph construction, Business systems, Financial system, Industry, Computer Science, Dynamic asset trees, Food webs, Financial networks, Markets, Graphs

Citation

Çavdur, F. ve Kumara, S. R. (2014). "Network mining: Applications to business data". Information Systems Frontiers, 16(3), 473-490.

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