We develop a network reconstruction model based on the entropy maximization considering the sparsity of network. Here the reconstruction is to estimate network's adjacency matrix from node's local information. We reconstruct the interbank network in Japan from financial data in balance sheets of individual banks using the developed reconstruction model in the period from 2000 to 2016. The  sparsity of the interbank network is successfully reproduced in the reconstructed network . We examine the accuracy of the reconstructed interbank network by comparing the actual data and analyze the characteristics of the interbank network . The comparison confirms that the accuracy of the reconstruction model is acceptably good. For the reconstructed interbank network, we obtain the following characteristics  which are consistent with the previously known stylized facts : the short path length, the small clustering coefficient, the disassortative property, and the core and peripheral structure . Community analysis shows that the number of communities is 2-3 in the normal period, 1 in the economic crisis (2003, 2008-2013). The major nodes in each community have been the major commercial banks. Since 2013, the major commercial banks have lost the average PageRank and the leading regional banks have obtained both the average degree and the average PageRank . The observed changing role of banks is considered as a result of the quantitative and qualitative easing monetary policy started by  Bank of Japan in April of 2013.
We develop a network reconstruction model based on entropy maximization considering the sparsity of networks. We reconstruct the interbank network in Japan from financial data in individual banks' balance sheets using the developed reconstruction model from 2000 to 2016. The observed sparsity of the interbank network is successfully reproduced . We examine the characteristics of the reconstructed interbank network by calculating important network attributes. We obtain the following characteristics , which are consistent with the previously known stylized facts . Although we do not introduce the mechanism to generate the core and peripheral structure , we impose the constraints to consider the sparsity that is no transactions within the same bank category except for major commercial banks, the core and peripheral structure has spontaneously emerged. We identify major nodes in each community using the value of PageRank and degree to examine the changing role of each bank category . The observed changing role of banks is considered a result of the quantitative and qualitative monetary easing policy started by the Bank of Japan in April 2013.
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the
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network. Here the reconstruction is to estimate network's adjacency matrix from node's local information.
networks.
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balance sheets of individual banks
individual banks' balance sheets
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in the period
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observed
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in the reconstructed network
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accuracy
characteristics
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comparing the actual data and analyze the characteristics of the interbank network . The comparison confirms that the accuracy of the reconstruction model is acceptably good. For the reconstructed interbank network, we
calculating important network attributes. We
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,
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: the short path length, the small clustering coefficient, the disassortative property, and the
. Although we do not introduce the mechanism to generate the
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. Community analysis shows that the number of communities is 2-3 in the normal period, 1 in the economic crisis (2003, 2008-2013). The
, we impose the constraints to consider the sparsity that is no transactions within the same bank category except for major commercial banks, the core and peripheral structure has spontaneously emerged. We identify
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have been the major commercial banks. Since 2013, the major commercial banks have lost the average PageRank and the leading regional banks have obtained both the average degree and the average PageRank
using the value of PageRank and degree to examine the changing role of each bank category
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as
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easing monetary
monetary easing
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the
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of