Estimating topological properties of weighted networks from limited
  information

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

A fundamental problem in studying and modeling economic and financial systems is represented by privacy issues, which put severe limitations on the amount of accessible information. Here we introduce a novel, highly nontrivial method to reconstruct the structural properties of complex weighted networks of this kind using only partial information: the total number of nodes and links, and the values of the strength for all nodes. The latter are used as fitness to estimate the unknown node degrees through a standard configuration model. Then, these estimated degrees and the strengths are used to calibrate an enhanced configuration model in order to generate ensembles of networks intended to represent the real system. The method, which is tested on real economic and financial networks, while drastically reducing the amount of information needed to infer network properties, turns out to be remarkably effective\(-\)thus representing a valuable tool for gaining insights on privacy-protected socioeconomic systems.

Related collections

Most cited references 2

Record: found
Abstract: found
Article: found

Is Open Access

Hierarchical structure and the prediction of missing links in networks

Aaron Clauset, M. Newman, Cristopher Moore (2008)

Networks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science. Recent studies suggest that networks often exhibit hierarchical organization, where vertices divide into groups that further subdivide into groups of groups, and so forth over multiple scales. In many cases these groups are found to correspond to known functional units, such as ecological niches in food webs, modules in biochemical networks (protein interaction networks, metabolic networks, or genetic regulatory networks), or communities in social networks. Here we present a general technique for inferring hierarchical structure from network data and demonstrate that the existence of hierarchy can simultaneously explain and quantitatively reproduce many commonly observed topological properties of networks, such as right-skewed degree distributions, high clustering coefficients, and short path lengths. We further show that knowledge of hierarchical structure can be used to predict missing connections in partially known networks with high accuracy, and for more general network structures than competing techniques. Taken together, our results suggest that hierarchy is a central organizing principle of complex networks, capable of offering insight into many network phenomena.

0 comments Cited 179 times – based on 0 reviews

Preprint

     Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk

Stefano Battiston, Michelangelo Puliga, Rahul Kaushik … (2012)

Systemic risk, here meant as the risk of default of a large portion of the financial system, depends on the network of financial exposures among institutions. However, there is no widely accepted methodology to determine the systemically important nodes in a network. To fill this gap, we introduce, DebtRank, a novel measure of systemic impact inspired by feedback-centrality. As an application, we analyse a new and unique dataset on the USD 1.2 trillion FED emergency loans program to global financial institutions during 2008–2010. We find that a group of 22 institutions, which received most of the funds, form a strongly connected graph where each of the nodes becomes systemically important at the peak of the crisis. Moreover, a systemic default could have been triggered even by small dispersed shocks. The results suggest that the debate on too-big-to-fail institutions should include the even more serious issue of too-central-to-fail.

0 comments Cited 169 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Publication date Created: 2014-09-22

Publication date Updated: 2014-11-18

Article

DOI: 10.1103/PhysRevE.92.040802

ArXiV ID: 1409.6193

SO-VID: 980f072a-664e-4a2d-ac1b-7607de9aef40

License:

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

History

Custom metadata

Journal reference Phys. Rev. E 92, 040802 (2015)

Categories physics.soc-ph cond-mat.stat-mech cs.SI q-fin.ST

ScienceOpen disciplines: Social & Information networks,Condensed matter,General physics,Statistical finance

Data availability:

ScienceOpen disciplines: Social & Information networks, Condensed matter, General physics, Statistical finance

Estimating topological properties of weighted networks from limited information

Read this article at

Abstract

Related collections

An Introduction to SAXS

Most cited references 2

Hierarchical structure and the prediction of missing links in networks

DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk

Author and article information

Journal

Article

History

Custom metadata

Comments

Comment on this article

Similar content 20

Cited by 9

Most referenced authors 14