Evidence map›Paper›PMID 37072418›Full record

ArticleNature communications2023

Diffusion capacity of single and interconnected networks.

Tiago A Schieber, Laura C Carpi, Panos M Pardalos, Cristina Masoller, Albert Díaz-Guilera, Martín G Ravetti

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.8field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 17 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 4 countries.

Tiago A SchieberDepartamento de Ciências Administrativas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
Laura C CarpiInstituto Nacional de Ciência e Tecnologia, Sistemas Complexos, INCT-SC, CEFET-MG, Belo Horizonte, MG, Brazil.
Panos M PardalosIndustrial and Systems Engineering, University of Florida, Gainesville, FL, USA.
Cristina MasollerDepartament de Física, Universitat Politècnica de Catalunya, Terrassa, BCN, Spain.ORCID http://orcid.org/0000-0003-0768-2019
Albert Díaz-GuileraDepartament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, BCN, Spain.ORCID http://orcid.org/0000-0002-3114-0984
Martín G RavettiDepartamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil. martin@dcc.ufmg.br.ORCID http://orcid.org/0000-0001-7781-0034
Universidade Federal de Minas Gerais · BRNational Research University Higher School of Economics · RUUniversitat de Barcelona · ESUniversitat Politècnica de Catalunya · ES

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education) BrafitecGeneralitat de Catalunya (Government of Catalonia) ICREA ACADEMIAMinistry of Economy and Competitiveness | Consejo Superior de Investigaciones Científicas (Spanish National Research Council) PID2021-123994NB-C21
6 · The paper itself

Abstract

Understanding diffusive processes in networks is a significant challenge in complexity science. Networks possess a diffusive potential that depends on their topological configuration, but diffusion also relies on the process and initial conditions. This article presents Diffusion Capacity, a concept that measures a node's potential to diffuse information based on a distance distribution that considers both geodesic and weighted shortest paths and dynamical features of the diffusion process. Diffusion Capacity thoroughly describes the role of individual nodes during a diffusion process and can identify structural modifications that may improve diffusion mechanisms. The article defines Diffusion Capacity for interconnected networks and introduces Relative Gain, which compares the performance of a node in a single structure versus an interconnected one. The method applies to a global climate network constructed from surface air temperature data, revealing a significant change in diffusion capacity around the year 2000, suggesting a loss of the planet's diffusion capacity that could contribute to the emergence of more frequent climatic events.

Identifiers

PMID37072418
PMCPMC10113202
OpenAlexW4366334913

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.