ArticlePloS one2023
A memetic algorithm for finding multiple subgraphs that optimally cover an input network.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
Who cites it
2 citing papers in PubMed.
- Sparse Subsystem Discovery for Intelligent Sensor Networks.Sensors (Basel, Switzerland) · 2026Article
- Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Finding dense subgraphs is a central problem in graph mining, with a variety of real-world application domains including biological analysis, financial market evaluation, and sociological surveys. While a series of studies have been devoted to finding subgraphs with maximum density, the problem of finding multiple subgraphs that best cover an input network has not been systematically explored. The present study discusses a variant of the densest subgraph problem and presents a mathematical model for optimizing the total coverage of an input network by extracting multiple subgraphs. A memetic algorithm that maximizes coverage is proposed and shown to be both effective and efficient. The method is applied to real-world networks. The empirical meaning of the optimal sampling method is discussed.
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Registered trials
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.