Bootstrapping Topological Properties and Systemic Risk of Complex Networks Using the Fitness Model

Musmeci, Nicoló; Battiston, Stefano; Caldarelli, Guido; Puliga, Michelangelo; Gabrielli, Andrea

doi:10.1007/s10955-013-0720-1

Cited by 78 publications

(79 citation statements)

References 21 publications

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“…Consequently, similarity indices may be local (like the Adamic-Adar index, common neighbors index, hub promoted index, hub suppressed index, Jaccard index, Leicht-Holme-Newman index, preferential attachment index, resource allocation index, Salton index, or the Sørensen index) mesoscopic (like the local path index or the local random walk index), or global (like the average commute time index, cosine-based index, Katz index, Leicht-Holme-Newman index, matrix forest index, random walk with restart index, or the SimRank index). Edge neighborhood may be compared by using the network degree, preferential attachment methods, fitness values, community structure, network hierarchy, a stochastic bloc model, a probabilistic model, or by using hypergraphs (Albert & Albert, 2004; Liben-Novell & Kleinberg 2007; Yan et al, 2007a; Guimerà & Sales-Pardo, 2009; Lü et al, 2009; Zhou et al, 2009; Chen et al, 2012a; Eronen & Toivonen, 2012; Hu et al, 2012; Musmeci et al, 2012; Yan & Gregory, 2012; Liu et al, 2013). It is important to note that methods may perform differently, if the missing edge is in a dense network core or in a sparsely connected network periphery (Zhu et al, 2012a).…”

Section: An Inventory Of Network Analysis Tools Helping Drug Designmentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

808

661

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Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

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Section: An Inventory Of Network Analysis Tools Helping Drug Designmentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

808

661

View full text Add to dashboard Cite

show abstract

“…Furthermore, our approach is Bayesian, making it possible to learn properties of the network from observed information. Musmeci et al (2013) consider the problem of reconstructing topological properties from limited information. They use a bootstrapping approach and decide on link existence via a fitness model.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Methodology for Systemic Risk Assessment in Financial Networks

Veraart

2015

SSRN Journal

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We develop a Bayesian methodology for systemic risk assessment in financial networks such as the interbank market. Nodes represent participants in the network and weighted directed edges represent liabilities. Often, for every participant, only the total liabilities and total assets within this network are observable. However, systemic risk assessment needs the individual liabilities. We propose a model for the individual liabilities, which, following a Bayesian approach, we then condition on the observed total liabilities and assets and, potentially, on certain observed individual liabilities. We construct a Gibbs sampler to generate samples from this conditional distribution. These samples can be used in stress testing, giving probabilities for the outcomes of interest. As one application we derive default probabilities of individual banks and discuss their sensitivity with respect to prior information included to model the network. An R-package implementing the methodology is provided.

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“…Many researchers studied aggregate networks not necessarily because it conveys information about the interconnected risk structure, but rather [91] because it would reveal meaningful information about the structure of long-term relationships among banks. Another possible reason for why aggregate networks attract attention is that daily networks can be much sparser and noisier than aggregate networks, and they appear to change their structure from day to day in a purely random manner [93,94].…”

Section: Why Daily Scale?mentioning

confidence: 99%

Network models of financial systemic risk: a review

2017

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The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible financial linkages. Recently, a lot of attention has been paid to the understanding of the mechanisms that can lead to a breakdown of this network. This can happen when the existing financial links turn from being a means of risk diversification to channels for the propagation of risk across financial institutions. In this review article, we summarize recent developments in the modeling of financial systemic risk. We focus, in particular, on network approaches, such as models of default cascades due to bilateral exposures or to overlapping portfolios, and we also report on recent findings on the empirical structure of interbank networks. The current review provides a landscape of the newly arising interdisciplinary field lying at the intersection of several disciplines, such as network science, physics, engineering, economics, and ecology.

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Bootstrapping Topological Properties and Systemic Risk of Complex Networks Using the Fitness Model

Cited by 78 publications

References 21 publications

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

A Bayesian Methodology for Systemic Risk Assessment in Financial Networks

Network models of financial systemic risk: a review

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