Nestedness Maximization in Complex Networks through the Fitness-Complexity Algorithm

Lin, Jianhong; Tessone, Claudio J.

doi:10.3390/e20100768

Cited by 18 publications

(19 citation statements)

References 40 publications

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“…1 should be mainly attributed to the fact that eigenvectors of different order are considered in the two approaches. Hence, the two metrics bring different information; albeit different, this information is relevant for both metrics, as demonstrated by numerous practical applications of the two approaches [20][21][22]24,[30][31][32][33] .…”

Section: Resultsmentioning

confidence: 99%

Reconciling contrasting views on economic complexity

et al. 2020

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Summarising the complexity of a country's economy in a single number is the holy grail for scholars engaging in data-based economics. In a field where the Gross Domestic Product remains the preferred indicator for many, economic complexity measures, aiming at uncovering the productive knowledge of countries, have been stirring the pot in the past few years. The commonly used methodologies to measure economic complexity produce contrasting results, undermining their acceptance and applications. Here we show that these methodologiesapparently conflicting on fundamental aspectscan be reconciled by adopting a neat mathematical perspective based on linear-algebra tools within a bipartite-networks framework. The obtained results shed new light on the potential of economic complexity to trace and forecast countries' innovation potential and to interpret the temporal dynamics of economic growth, possibly paving the way to a micro-foundation of the field.

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Section: Resultsmentioning

confidence: 99%

Reconciling contrasting views on economic complexity

et al. 2020

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“…They found that "country-product" bipartite networks are significantly nested [87], and the dynamics of nestedness can predict the evolution of industrial ecosystems (see Refs. [88,89] for details on nestedness in networks). Moreover, the nestedness tends to be constant over time, making the pattern of industrial appearances predictable.…”

Section: Product Space and Economic Complexitymentioning

confidence: 99%

Computational Socioeconomics

Gao,

Zhang,

Zhou

2019

Preprint

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Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies.

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“…This ordering leads to the characteristic triangular shape (Medan et al., 2007) shown in Figure 1. The arrangement of the matrix which reveals and maximizes its nestedness is usually referred to as a maximally packed configuration , and various methods to produce it can be found in the literature (Domínguez‐García & Munoz, 2015; Lin, Tessone, & Mariani, 2018; Rodríguez‐Gironés & Santamaría, 2006).…”

Section: Introductionmentioning

confidence: 99%