Evidence map›Paper›PMID 30443578›Full record

ArticleApplied network science2017

Multiplex flows in citation networks.

Benjamin Renoust, Vivek Claver, Jean-François Baffier

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Applied network science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.3field-weighted citation impact, top 11% 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, 13 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 2 countries.

Benjamin Renoust1National Institute of Informatics, Tokyo, Japan.ORCID 0000-0003-2692-9056
Vivek Claver1National Institute of Informatics, Tokyo, Japan.
Jean-François Baffier1National Institute of Informatics, Tokyo, Japan.
International Research Center for Japanese Studies · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Knowledge is created and transmitted through generations, and innovation is often seen as a process generated from collective intelligence. There is rising interest in studying how innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherits. A citation network can be seen as a perfect example of one generative process leading to innovation. However, the impact and influence of scientific publication are always difficult to capture and measure. We offer a new take on investigating how the knowledge circulates and is transmitted, inspired by the notion of "stream of knowledge". We propose to look at this question under the lens of flows in directed acyclic graphs (DAGs). In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the

Indexed as

Citation networkDirected acyclic graph (DAG)Flow of knowledgeLarge networkMetricsMultiplex network

Identifiers

PMID30443578
PMCPMC6214274
OpenAlexW2739156609

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.