Evidence map›Paper›PMID 37680955›Full record

ArticleFrontiers in big data2023

Efficient community detection in multilayer networks using boolean compositions.

Abhishek Santra, Fariba Afrin Irany, Kamesh Madduri, Sharma Chakravarthy, Sanjukta Bhowmick

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in big data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.2field-weighted citation impact, top 24% 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

0 citing papers in PubMed, 7 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 3 institutions in 1 country.

Abhishek SantraDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.
Fariba Afrin IranyDepartment of Computer Science and Engineering, University of North Texas, Denton, TX, United States.
Kamesh MadduriDepartment of Computer Science and Engineering, The Pennsylvania State University, State College, PA, United States.
Sharma ChakravarthyDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.
Sanjukta BhowmickDepartment of Computer Science and Engineering, University of North Texas, Denton, TX, United States.
The University of Texas at Arlington · USUniversity of North Texas · USPennsylvania State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Networks (or graphs) are used to model the dyadic relations between entities in complex systems. Analyzing the properties of the networks reveal important characteristics of the underlying system. However, in many disciplines, including social sciences, bioinformatics, and technological systems, multiple relations exist between entities. In such cases, a simple graph is not sufficient to model these multiple relations, and a multilayer network is a more appropriate model. In this paper, we explore community detection in multilayer networks. Specifically, we propose a novel

Indexed as

boolean combinationcommunity detectionhomogeneous networksmultilayer networknetwork decoupling

Identifiers

PMID37680955
PMCPMC10481956
OpenAlexW4386090655

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.