Evidence map›Paper›PMID 29459635›Full record

ArticleScientific reports2018

Multiresolution Consensus Clustering in Networks.

Lucas G S Jeub, Olaf Sporns, Santo Fortunato

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed
13.6field-weighted citation impact, top 1% 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

73 citing papers in PubMed, 167 citations in OpenAlex.

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  3. Structural signatures of synergy and redundancy in human brain function.bioRxiv : the preprint server for biology · 2026
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  5. Intrinsic neuronal network organization of a mammalian peripheral nervous system.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  7. The intrinsic neuronal network of the central nervous system and its modular (subsystem) architecture in a mammal.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  8. Review
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  14. Article
  15. Neural network architecture of a mammalian brain.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  16. Article
  17. Article
  18. Brain metabolic network covariance and aging in a mouse model of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  19. Article
  20. Network architecture of intrinsic connectivity in a mammalian spinal cord (the central nervous system's caudal sector).Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article

13 more citing papers are in PubMed but not listed here.

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 2 institutions in 1 country.

Lucas G S JeubSchool of Informatics, Computing and Engineering, Indiana University, Indiana, United States. ljeub@iu.edu.
Olaf SpornsDepartment of Psychological and Brain Sciences, Indiana University, Indiana, United States.
Santo FortunatoSchool of Informatics, Computing and Engineering, Indiana University, Indiana, United States.
Indiana University of Pennsylvania · USIndiana University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Networks often exhibit structure at disparate scales. We propose a method for identifying community structure at different scales based on multiresolution modularity and consensus clustering. Our contribution consists of two parts. First, we propose a strategy for sampling the entire range of possible resolutions for the multiresolution modularity quality function. Our approach is directly based on the properties of modularity and, in particular, provides a natural way of avoiding the need to increase the resolution parameter by several orders of magnitude to break a few remaining small communities, necessitating the introduction of ad-hoc limits to the resolution range with standard sampling approaches. Second, we propose a hierarchical consensus clustering procedure, based on a modified modularity, that allows one to construct a hierarchical consensus structure given a set of input partitions. While here we are interested in its application to partitions sampled using multiresolution modularity, this consensus clustering procedure can be applied to the output of any clustering algorithm. As such, we see many potential applications of the individual parts of our multiresolution consensus clustering procedure in addition to using the procedure itself to identify hierarchical structure in networks.

Identifiers

PMID29459635
PMCPMC5818657
OpenAlexW2963041679

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.