An Information-Theoretic Bound on

Predrag Obradovic, Vladimir Kovačević, Xiqi Li, Aleksandar Milosavljevic

Open access · goldAbstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact, top 89% 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, 0 citations in OpenAlex.

ID-GBA: Subgraph Extension With Information Distance Guilt by Association in Complex Networks.IEEE access : practical innovations, open solutions · 2025
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 at 2 institutions in 2 countries.

Predrag ObradovicSchool of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.ORCID 0000-0001-8570-053X

Vladimir KovačevićSchool of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.ORCID 0000-0002-9843-6261

Xiqi LiDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.ORCID 0000-0003-0875-8337

Aleksandar MilosavljevicDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.ORCID 0000-0001-5482-2825

Baylor College of Medicine · USUniversity of Belgrade · RS

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extraction of subsets of highly connected nodes ("communities" or modules) is a standard step in the analysis of complex social and biological networks. We here consider the problem of finding a relatively small set of nodes in two labeled weighted graphs that is highly connected in both. While many scoring functions and algorithms tackle the problem, the typically high computational cost of permutation testing required to establish the

Indexed as

community detectionconnectednessinformation theorypattern discovery

Identifiers

PMID37420347

DOI10.3390/e24101329

PMCPMC9601153

OpenAlexW4296897681

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.