Evidence map›Paper›PMID 29186160›Full record

ArticlePloS one2017

Community detection in dynamic networks via adaptive label propagation.

Jihui Han, Wei Li, Longfeng Zhao, Zhu Su, Yijiang Zou, Weibing Deng

Abstract read
In one paragraph

Article in PloS one, 2017. 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
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.

  1. 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

6 authors.

Jihui HanComplexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China.ORCID http://orcid.org/0000-0001-9402-9020
Wei LiComplexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China.
Longfeng ZhaoComplexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China.
Zhu SuComplexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China.
Yijiang ZouComplexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China.
Weibing DengComplexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.

Indexed as

AlgorithmsCommunity Networks

Identifiers

PMID29186160
PMCPMC5706735

What OpenQuestion holds

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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.