
doi: 10.3390/sym10090404
Hypergraph theory is the most developed tool for demonstrating various practical problems in different domains of science and technology. Sometimes, information in a network model is uncertain and vague in nature. In this paper, our main focus is to apply the powerful methodology of fuzziness to generalize the notion of competition hypergraphs and fuzzy competition graphs. We introduce various new concepts, including fuzzy column hypergraphs, fuzzy row hypergraphs, fuzzy competition hypergraphs, fuzzy k-competition hypergraphs and fuzzy neighbourhood hypergraphs, strong hyperedges, kth strength of competition and symmetric properties. We design certain algorithms for constructing different types of fuzzy competition hypergraphs. We also present applications of fuzzy competition hypergraphs in decision support systems, including predator–prey relations in ecological niche, social networks and business marketing.
ecological niches, Management decision making, including multiple objectives, Ecology, fuzzy open neighbourhood, Hypergraphs, Fractional graph theory, fuzzy graph theory, fuzzy competition hypergraph; fuzzy k-competition hypergraph; fuzzy open neighbourhood; fuzzy closed neighbourhood; ecological niches, Decision theory, fuzzy competition hypergraph, Graph algorithms (graph-theoretic aspects), fuzzy \(k\)-competition hypergraph, fuzzy closed neighbourhood, Social networks; opinion dynamics
ecological niches, Management decision making, including multiple objectives, Ecology, fuzzy open neighbourhood, Hypergraphs, Fractional graph theory, fuzzy graph theory, fuzzy competition hypergraph; fuzzy k-competition hypergraph; fuzzy open neighbourhood; fuzzy closed neighbourhood; ecological niches, Decision theory, fuzzy competition hypergraph, Graph algorithms (graph-theoretic aspects), fuzzy \(k\)-competition hypergraph, fuzzy closed neighbourhood, Social networks; opinion dynamics
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 24 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
