ArticleScientific reports2018
Compressing Networks with Super Nodes.
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 7 papers, 1 of them a synthesis that pooled it.
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.
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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.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria.Sensors (Basel, Switzerland) · 2022Pooled it
- Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification.bioRxiv : the preprint server for biology · 2026Article
- Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning.Nature biomedical engineering · 2022Article
- Simplification of networks by conserving path diversity and minimisation of the search information.Scientific reports · 2020Article
- Clustering analysis of tumor metabolic networks.BMC bioinformatics · 2020Article
- Functional network analysis reveals an immune tolerance mechanism in cancer.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Construction, comparison and evolution of networks in life sciences and other disciplines.Journal of the Royal Society, Interface · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network as a smaller network of 'super nodes', where each super node comprises one or more nodes of the original network. We can then use this super node representation as the input into standard community detection algorithms. To define the seeds, or centers, of our super nodes, we apply the 'CoreHD' ranking, a technique applied in network dismantling and decycling problems. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity and more stable across multiple (stochastic) runs within and between community detection algorithms, yet still overlap well with the results obtained using the full network.
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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.