Information filtering in complex weighted networks

Many systems in nature, society and technology can be described as networks, where the vertices are the system's elements and edges between vertices indicate the interactions between the corresponding elements. Edges may be weighted if the interaction strength is measurable. However, the full network information is often redundant because tools and techniques from network analysis do not work or become very inefficient if the network is too dense and some weights may just reflect measurement errors, and shall be discarded. Moreover, since weight distributions in many complex weighted networks are broad, most of the weight is concentrated among a small fraction of all edges. It is then crucial to properly detect relevant edges. Simple thresholding would leave only the largest weights, disrupting the multiscale structure of the system, which is at the basis of the structure of complex networks, and ought to be kept. In this paper we propose a weight filtering technique based on a global null model (GloSS filter), keeping both the weight distribution and the full topological structure of the network. The method correctly quantifies the statistical significance of weights assigned independently to the edges from a given distribution. Applications to real networks reveal that the GloSS filter is indeed able to identify relevantconnections between vertices.Comment: 9 pages, 7 figures, 1 Table. The GloSS filter is implemented in a freely downloadable software (http://filrad.homelinux.org/resources

Backbone of credit relationships in the Japanese credit market

We detect the backbone of the weighted bipartite network of the Japanese credit market relationships. The backbone is detected by adapting a general method used in the investigation of weighted networks. With this approach we detect a backbone that is statistically validated against a null hypothesis of uniform diversification of loans for banks and firms. Our investigation is done year by year and it covers more than thirty years during the period from 1980 to 2011. We relate some of our findings with economic events that have characterized the Japanese credit market during the last years. The study of the time evolution of the backbone allows us to detect changes occurred in network size, fraction of credit explained, and attributes characterizing the banks and the firms present in the backbone.Comment: 14 pages, 8 figure

Analysis of Global Banking Network

The outbreak of the Global Pandemic Covid-19 that spread terribly across various countries from the end of 2019, has severely altered people’s life and economy. Various reports across papers and news articles on how each government was managing the costs of vaccines, medical equipment, and necessities. The world saw shifts in stock markets, unemployment, the tourism industry completely coming to a standstill, and more. Has this Covid Pandemic which played a crucial role within geographical boundaries altered the financial transactions across countries on a higher level? With the help of the statistics available with the Bank of International Settlements, this project aims to analyze the cross-border lending pattern across countries. This can be analyzed with the help of Complex Network analysis. The network reflects the data where the nodes are the countries and bilateral links correspond to credit linkages. Using various topological network measures such as Degree, Strength, Clustering coefficient, and Polya Filter, we can analyze the financial interconnectedness and the possibility of change in network patterns during times of crisis such as Covid-19. This will help to find a correlation between this sudden worldwide crisis and the lending market among banks

A stochastic generative model of the World Trade Network.

The World Trade Network (WTN) is a network of exchange flows among countries whose topological and statistical properties are a valuable source of information. Degree and strength (weighted degree) are key magnitudes to understand its structure and generative mechanisms. In this work, we describe a stochastic model that yields synthetic networks that closely mimic the properties of annual empirical data. The model combines two popular mechanisms of network generation: preferential attachment and multiplicative process. Agreement between empirical and synthetic networks is checked using the available series from 1962 to 2017.post-print2516 K