@inproceedings{9e131bf41bb248ceb789530863ff0bec,
title = "Complex interbank network estimation: sparsity-clustering threshold",
abstract = "In the “too-interconnected-to-fail” discussion network theory has emerged as an important tool to identify risk concentrations in interbank networks. Therefore, however, data on bilateral bank exposures, i.e. the edges in such a network, is not available but has to be estimated. In this work we report on the possibility of enhancing existing inference techniques with prior knowledge on network topology in order to preserve complex interbank network characteristics. A convenient feature of our technique is that a single parameter α governs the characteristics of the resulting network. In an empirical study we reconstruct the network of about 2100 US commercial banks and show that complex network characteristics can indeed be preserved and, moreover, controlled by α. In an outlook we discuss the possibility of developing an α -based measurement for the complexity characteristics of observed interbank networks.",
keywords = "Interbank networks, Network estimation, Node clustering, Sparse networks",
author = "Nils Bundi and Khaldoun Khashanah",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018 ; Conference date: 11-12-2018 Through 13-12-2018",
year = "2019",
doi = "10.1007/978-3-030-05414-4_39",
language = "English",
isbn = "9783030054137",
series = "Studies in Computational Intelligence",
pages = "487--498",
editor = "Aiello, {Luca Maria} and Hocine Cherifi and Pietro Li{\'o} and Rocha, {Luis M.} and Chantal Cherifi and Renaud Lambiotte",
booktitle = "Complex Networks and Their Applications VII - Volume 2 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018",
}