文献类型：期刊文献

英文题名：Comparative analysis of layered structures in empirical investor networks and cellphone communication networks

作者：Wang, Peng[1]; Ma, Jun-Chao[1]; Jiang, Zhi-Qiang[1]; Zhou, Wei-Xing[1,2]; Sornette, Didier[3,4]

机构：[1] School of Business and Research Center for Econophysics, East China University of Science and Technology, Shanghai, 200237, China; [2] School of Science, East China University of Science and Technology, Shanghai, 200237, China; [3] Department of Management, Technology and Economics, ETH Zurich, Scheuchzerstrasse 7, Zurich, CH-8092, Switzerland; [4] Swiss Finance Institute, University of Geneva, 40 blvd. Du Pont d'Arve, Geneva, CH-1211

年份：2019

外文期刊名：arXiv

收录：EI(收录号：20200283587)

语种：英文

外文关键词：K-means clustering - Network layers - Normal distribution - Telecommunication networks - Telephone sets

摘要：Empirical investor networks (EIN) proposed by Ozsoylev et al. (2014) are assumed to capture the information spreading path among investors. Here, we perform a comparative analysis between the EIN and the cellphone communication networks (CN) to test whether EIN is an information exchanging network fromthe perspective of the layer structures of ego networks. We employ two clustering algorithms (k-means algorithm and H/T break algorithm) to detect the layer structures for each node in both networks. We find that the nodes in both networks can be clustered into two groups, one that has a layer structure similar to the theoretical Dunbar Circle corresponding to that the alters in ego networks exhibit a four-layer hierarchical structure with the cumulative number of 5, 15, 50 and 150 from the inner layer to the outer layer, and the other one having an additional inner layer with about 2 alters compared with the Dunbar Circle. We also find that the scale ratios, which are estimated based on the unique parameters in the theoretical model of layer structures (Tamarit et al., 2018), conform to a log-normal distribution for both networks. Our results not only deepen our understanding on the topological structures of EIN, but also provide empirical evidence of the channels of information diffusion among investors. Copyright ? 2019, The Authors. All rights reserved.