On Metzler Positive Systems on Hypergraphs
In graph-theoretical terms, an edge in a graph connects two vertices while a hyperedge of a hypergraph connects any more than one vertices. If the hypergraph's hyperedges further connect the same number of vertices, it is said to be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uniform</i>. In algebraic graph theory, a graph can be characterized by an adjacency matrix, and similarly, a uniform hypergraph can be characterized by an adjacency <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tensor</i>. This similarity enables us to extend existing tools of matrix analysis for studying dynamical systems evolving on graphs to the study of a class of polynomial dynamical systems evolving on hypergraphs utilizing the properties of tensors. To be more precise, in this article, we first extend the concept of a Metzler matrix to a Metzler tensor and then describe some useful properties of such tensors. Next, we focus on positive systems on hypergraphs associated with Metzler tensors. More importantly, we design control laws to stabilize the origin of this class of Metzler positive systems on hypergraphs. In the end, we apply our findings to two classic dynamical systems: a higher order Lotka–Volterra population dynamics system and a higher order SIS epidemic dynamic process. The corresponding novel stability results are accompanied by ample numerical examples.
Authors
- Ming Cao (ORCID: https://orcid.org/0000-0001-5472-562X)
- Hildeberto Jardón-Kojakhmetov (ORCID: https://orcid.org/0000-0001-8708-7409)
- Shaoxuan Cui (ORCID: https://orcid.org/0000-0002-2767-7244)
- Guofeng Zhang (ORCID: https://orcid.org/0000-0001-5854-5247)
Institutions
- Hong Kong Polytechnic University (HK)
- University of Groningen (NL)
Publication Details
- Journal
- IEEE Transactions on Control of Network Systems
- Published
- 2025-03-05
- DOI
- https://doi.org/10.1109/tcns.2025.3548300
- Citations
- 7
- Primary Topic
- Gene Regulatory Network Analysis
- Type
- article
- Field-Weighted Citation Impact
- 5.64