ArticleBMC systems biology2018
A systematic survey of centrality measures for protein-protein interaction networks.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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89 citing papers in PubMed.
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- Half-Space Proximal Networks (HSPNs): A Proxy for Multi-Query Similarity Searching Models Predicting Tumor-Homing Peptides.ACS omega · 2025Article
- In Silico Network Pharmacology, Molecular Docking, and Molecular Dynamics Analysis of Rosemary-Derived Compounds as Potential HSP90 Inhibitors for Cancer Therapy.Current issues in molecular biology · 2025Article
- [1]The human gut microbiota in IBD, characterizing hubs, the core microbiota and terminal nodes: a network-based approach.BMC microbiology · 2025Article
- Candidate Key Proteins of Tinnitus in the Auditory and Motor Systems of the Thalamus.International journal of molecular sciences · 2025Article
- Analysing health misinformation with advanced centrality metrics in online social networks.PLOS digital health · 2025Article
- Assessing the impact of sampling bias on node centralities in synthetic and biological networks.NPJ systems biology and applications · 2025Article
- A Multi-Modal Graph Neural Network Framework for Parkinson's Disease Therapeutic Discovery.International journal of molecular sciences · 2025Article
- Modeling the Interactions Between Chemicals and Proteins to Predict the Health Consequences of Air Pollution.International journal of environmental research and public health · 2025Article
- Computational Analysis of Virus-Host Interactomes.Methods in molecular biology (Clifton, N.J.) · 2025Review
- From variability to stability: Sensitivity of network properties in IBD human gut microbiome studies.Computational and structural biotechnology journal · 2025Article
29 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
backgroundNumerous centrality measures have been introduced to identify "central" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.
resultsWe used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network's topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.
conclusionsThe choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.
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