ArticleScientific reports2023
Mathematical modeling and topological graph description of dominating David derived networks based on edge partitions.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Integrating fuzzy logic and neural networks with multi-criteria decision-making for intelligent evaluation of drug compound design attributes.Scientific reports · 2026Article
- ChemGraphX: an open-source web tool for computing topological indices and entropy measures.Journal of cheminformatics · 2026Article
- Hydrogen-centric machine learning approach for analyzing properties of tricyclic anti-depressant drugs.Frontiers in chemistry · 2025Article
- On degree-based operators and topological descriptors of molecular graphs and their applications to QSPR analysis of carbon derivatives.Scientific reports · 2024Article
- Exploring the role of topological descriptors to predict physicochemical properties of anti-HIV drugs by using supervised machine learning algorithms.BMC chemistry · 2024Article
- Article
- Reduced reverse degree-based topological indices of graphyne and graphdiyne nanoribbons with applications in chemical analysis.Scientific reports · 2024Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical graph theory is a well-established discipline within chemistry that employs discrete mathematics to represent the physical and biological characteristics of chemical substances. In the realm of chemical compounds, graph theory-based topological indices are commonly employed to depict their geometric structure. The main aim of this paper is to investigate the degree-based topological indices of dominating David derived networks (DDDN) and assess their effectiveness. DDDNs are widely used in analyzing the structural and functional characteristics of complex networks in various fields such as biology, social sciences, and computer science. We considered the F
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Registered trials
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