Zhiyuan Zhang 0011

dblp:72/1760-11 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2025
0000-0003-3945-5638ORCID · corroborated

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Theory of computation · 3 · 3 since 2021
YearPublicationVenuePosition
2025 Hypergraph burning, matchings, and zero forcing
abstract
Lazy burning is a recently introduced variation of burning where only one set of vertices is chosen to burn during the first round. In hypergraphs, lazy burning spreads when all but one vertex in a hyperedge is burned. The lazy burning number is the minimum number of initially burned vertices that eventually burn all vertices. We give several equivalent characterizations of lazy burning on hypergraphs using matchings and zero forcing, and then apply these to establish new bounds and complexity results. We prove that the lazy burning number of a hypergraph H equals its order minus the maximum cardinality of a certain matching on its incidence graph. Using this characterization, we give a formula for the lazy burning number of a dual hypergraph and give new bounds on the lazy burning number based on various hypergraph parameters. We show that the lazy burning number of a hypergraph may be characterized by a maximal subhypergraph that results from iteratively deleting vertices in singleton hyperedges. We prove that lazy burning on a hypergraph is equivalent to zero forcing on its incidence graph and show an equivalence between skew zero forcing on a graph and lazy burning on its neighborhood hypergraph. As a result, we show that the decision problem of computing the lazy burning number of a hypergraph is NP-complete, which solves an open problem in [12] . By applying the results found for lazy burning, we show that the decision problem of computing the skew zero forcing number for bipartite graphs is NP-complete. We finish with open problems.
Anthony Bonato, Caleb Jones, Trent Marbach, Teddy Mishura, Zhiyuan Zhang 0011
Theor. Comput. Sci.5
2024 Clique Counts for Network Similarity
Anthony Bonato, Zhiyuan Zhang 0011
WAW2
2022 An Evolving Network Model from Clique Extension
Anthony Bonato, Ryan Cushman, Trent Marbach, Zhiyuan Zhang 0011
COCOON4