Evidence map›Paper›PMID 36832730›Full record

ArticleEntropy (Basel, Switzerland)2023

Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks.

Vasily Lubashevskiy, Seval Yurtcicek Ozaydin, Fatih Ozaydin

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

Article in Entropy (Basel, Switzerland), 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
1.8field-weighted citation impact, top 17% 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

2 citing papers in PubMed, 11 citations in OpenAlex.

  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

3 authors at 3 institutions in 2 countries.

Vasily LubashevskiyInstitute for International Strategy, Tokyo International University, 1-13-1 Matoba-kita, Kawagoe 350-1197, Saitama, Japan.ORCID 0000-0002-2399-5050
Seval Yurtcicek OzaydinDepartment of Social and Human Sciences, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan.ORCID 0000-0003-2429-0631
Fatih OzaydinInstitute for International Strategy, Tokyo International University, 1-13-1 Matoba-kita, Kawagoe 350-1197, Saitama, Japan.ORCID 0000-0002-5872-9158
European Organization for Nuclear Research · CHTokyo Institute of Technology · JPTokyo International University · JP

Funding

Tokyo International University Personal Research Fund
6 · The paper itself

Abstract

Discovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly improved version of the Link Entropy method. Using Louvain, Leiden and Walktrap methods, our proposal detects the number of communities in each iteration on discovering the communities. Running experiments on various benchmark networks, we show that our proposed method outperforms the Link Entropy method in quantifying edge significance. Considering also the computational complexities and possible defects, we conclude that Leiden or Louvain algorithms are the best choice for community number detection in quantifying edge significance. We also discuss designing a new algorithm for not only discovering the number of communities, but also computing the community membership uncertainties.

Indexed as

deep link entropyedge significanceLeidenlink entropyLouvainsocial networksWalktrap

Identifiers

PMID36832730
PMCPMC9954822
OpenAlexW4321095126

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.