Evidence map›Paper›PMID 39147920›Full record

ArticleScientific reports2024

Analyzing world city network by graph convolutional networks.

Linfang Tian, Weixiong Rao, Kai Zhao, Huy T Vo

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Linfang TianSchool of Software Engineering, Tongji University, Shanghai, 201804, China. tianlinfang@tongji.edu.cn.
Weixiong RaoSchool of Software Engineering, Tongji University, Shanghai, 201804, China.
Kai ZhaoJ. Mack Robinson College of Business, Georgia State University, Atlanta, 30301, USA.
Huy T VoThe City College of New York, City University of New York and New York University, New York, 10031, USA.

Funding

National Key Research and Development Program of China 2022YFE0208000
6 · The paper itself

Abstract

British scholar Peter Taylor constructed the World City Network by analyzing the office networks of multinational companies, enabling a network perspective on world cities. However, this method has long been hindered by data deficiencies and update delays. In this study, we utilized publicly available, real-time updated data on global routes to construct the World City Network, thereby addressing the issues of data insufficiency and delayed updates in the existing model. For the first time, advanced Graph Convolutional Networks were employed to analyze the World City Network, and we introduced GCNRank. Finally, we compared GCNRank with other centrality measures and found that GCNRank provides a more detailed representation of city rankings and effectively avoids local optima.

Identifiers

PMID39147920
PMCPMC11327301

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.