Evidence map›Paper›PMID 37876903›Full record

ArticlePatterns (New York, N.Y.)2023

EasyGraph: A multifunctional, cross-platform, and effective library for interdisciplinary network analysis.

Min Gao, Zheng Li, Ruichen Li, Chenhao Cui, Xinyuan Chen, Bodian Ye, Yupeng Li, Weiwei Gu, Qingyuan Gong, Xin Wang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Patterns (New York, N.Y.), 2023. 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. Review
  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

11 authors.

Min GaoShanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Zheng LiShanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Ruichen LiShanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Chenhao CuiShanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Xinyuan ChenShanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Bodian YeShanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Yupeng LiDepartment of Interactive Media, Hong Kong Baptist University, Hong Kong, China.
Weiwei GuCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China.
Qingyuan GongShanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Xin WangShanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Yang ChenShanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Networks are powerful tools for representing the relationships and interactions between entities in various disciplines. However, existing network analysis tools and packages either lack powerful functionality or are not scalable for large networks. In this descriptor, we present EasyGraph, an open-source network analysis library that supports several network data formats and powerful network mining algorithms. EasyGraph provides excellent operating efficiency through a hybrid Python/C++ implementation and multiprocessing optimization. It is applicable to various disciplines and can handle large-scale networks. We demonstrate the effectiveness and efficiency of EasyGraph by applying crucial metrics and algorithms to random and real-world networks in domains such as physics, chemistry, and biology. The results demonstrate that EasyGraph improves the network analysis efficiency for users and reduces the difficulty of conducting large-scale network analysis. Overall, it is a comprehensive and efficient open-source tool for interdisciplinary network analysis.

Indexed as

hybrid Python/C++ programminginterdisciplinary network analysismultiprocessing optimizationstructural hole theory

Identifiers

PMID37876903
PMCPMC10591136

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.