Reciprocity in directed networks

Yin, Mei; Zhu, Lingjiong

doi:10.1016/j.physa.2015.12.008

Cited by 7 publications

(4 citation statements)

References 50 publications

(73 reference statements)

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“…In some other cases, it may be possible to get approximate analytic solutions using a variety of techniques (mean-field theory, saddle-point approximation, diagrammatic perturbation theory, path integral representations). These situations have been vastly explored in the literature, and include the degree-correlated network model [115], the reciprocity model and the two-star model [116,117], the Strauss model of clustering [118], models of social collaboration [119], models of community structure [31], hierarchical topologies [120], models with spatial embedding [121] and rich-club features [122], and finally model constraining both the degree distribution and degree-degree correlations-which are known under the name of Tailored Random Graphs [123][124][125]. At last, when any analytic approach for computing Z becomes intractable, the ensemble can be still populated using Monte Carlo simulations, either to explicitly sample the configuration space-taking care of avoiding sampling biases through the use of ergodic Markov chains fulfilling detailed balance [126][127][128], or to derive approximate maximum likelihood estimators-taking care of avoiding degenerate regions of the phase space leading to frequent trapping in local minima [129][130][131][132][133][134][135].…”

Section: Beyond Local Constraintsmentioning

confidence: 99%

The statistical physics of real-world networks

Cimini¹,

Squartini²,

Saracco³

et al. 2019

Nat Rev Phys

311

308

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In the last 15 years, statistical physics has been a very successful framework to model complex networks. On the theoretical side, this approach has brought novel insights into a variety of physical phenomena, such as self-organisation, scale invariance, emergence of mixed distributions and ensemble non-equivalence, that display unconventional features on heterogeneous networks. At the same time, thanks to their deep connection with information theory, statistical physics and the principle of maximum entropy have led to the definition of null models for networks reproducing some features of real-world systems, but otherwise as random as possible. We review here the statistical physics approach and the various null models for complex networks, focusing in particular on the analytic frameworks reproducing the local network features. We then show how these models have been used to detect statistically significant and predictive structural patterns in real-world networks, as well as to reconstruct the network structure in case of incomplete information. We further survey the statistical physics models that reproduce more complex, semi-local network features using Markov chain Monte Carlo sampling, as well as the models of generalised network structures such as multiplex networks, interacting networks and simplicial complexes. arXiv:1810.05095v2 [physics.soc-ph]

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Section: Beyond Local Constraintsmentioning

confidence: 99%

The statistical physics of real-world networks

Cimini¹,

Squartini²,

Saracco³

et al. 2019

Nat Rev Phys

311

308

View full text Add to dashboard Cite

show abstract

“…A large number of scholars have focused on the integration of exogenous mechanisms and endogenous structures of trade networks. Generally, reciprocal behavior is an important tendency in social relations, where countries in trade networks trade with other countries based on the theory of comparative advantage and reciprocity [ 35 , 36 ]. In addition, triangular structure (transitivity) is also considered a fundamental building block in international trade networks.…”

Section: Theoretical Framework and Assumptionsmentioning

confidence: 99%

Structural Characteristics and Evolutionary Drivers of Global Virtual Water Trade Networks: A Stochastic Actor-Oriented Model for 2000–2015

Xing

Cheng

2023

IJERPH

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The globalization of trade has caused tremendous pressure on water resources globally, and a virtual water trade provides a new perspective on global freshwater sharing and water sustainability. No study has yet explored the structural characteristics and drivers of the evolution of global virtual water trade networks from a network structure evolution perspective. This paper aims to fill this critical gap by developing a research framework to explore how endogenous network structures and external factors have influenced the evolution of virtual water trade networks. We constructed virtual water trade networks for 62 countries worldwide from 2000 to 2015 and used an innovative combination of multi-regional input–output data and stochastic actor-oriented models for analytical purposes. Our results support the theoretical hypothesis of ecologically unequal exchange and trade drivers, arguing that virtual water flows from less developed countries to developed countries under global free trade and that unequal trade patterns lead to excessive consumption of virtual water in less developed countries. The results partially support the theoretical content of water endowment and traditional gravity models, finding that trade networks are expanding to farther and larger markets, confirming that national water scarcity levels do not impact the evolution of virtual water trade networks. Finally, we point out that meritocratic links, path dependence, reciprocity, and transmissive links have extreme explanatory power for the evolutionary development of virtual water networks.

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“…Reciprocity is an important feature of directed networks that explains the relationships formed by nodes through feedback. Reciprocity is a measure of the simplest interaction processes occurring in the network and is widely used when modeling complex networks [53]. Garlaschelli and Loffredo [54] found that the detection of reciprocity helps reveal the formation mechanisms of the observed network topology and explain its organization principles.…”

Section: Structure-dependent Effectsmentioning

confidence: 99%

The Analysis of Risk Measurement and Association in China’s Financial Sector Using the Tail Risk Spillover Network

Yao

Zhang

2023

Mathematics

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This study focused on analyzing the complexities and risk spillovers that arise among financial institutions due to the development of financial markets. The research employed the conditional value at risk (CoVaR) methodology to quantify the extent of tail risk spillover and constructed a risk spillover network encompassing Chinese financial institutions. The study further investigated the characteristics, transmission paths, and dynamic evolution of this network under different risk conditions. The empirical findings of this research highlighted several important insights. First, financial institutions play distinct roles in the risk spillover process, with the securities and banking sectors as risk exporters and the insurance and diversified financial sectors as risk takers. The closest risk spillover relationships were observed between banking and insurance and between securities and diversified financial sectors. Second, in high-risk scenarios, there is significant intrasectoral risk transmission between banks and the diversified financial sector, as well as dual-sectoral risk contagion between banks and securities, with the most-common transmission occurring between diversified financial and securities sectors. Finally, the securities sector acts as the pivotal node for risk spillovers, being the main transmitter of intersectoral risks. The formation and evolution of risk spillover networks are influenced by endogenous mechanisms, in particular the convergence effect.

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Reciprocity in directed networks

Cited by 7 publications

References 50 publications

The statistical physics of real-world networks

The statistical physics of real-world networks

Structural Characteristics and Evolutionary Drivers of Global Virtual Water Trade Networks: A Stochastic Actor-Oriented Model for 2000–2015

The Analysis of Risk Measurement and Association in China’s Financial Sector Using the Tail Risk Spillover Network

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