Aug 2026· Journal of Advances in Mathematics and Computer Science· Vol 41, pp. 18-25· 0 citations
Abstract
Hypergraphs extend ordinary graphs by allowing a hyperedge to connect more than two vertices. A hypergraph is k -uniform when each hyperedge contains exactly k vertices, and it is loose cyclic when the hyperedges are arranged cyclically so that consecutive hyperedges share exactly one vertex while non-consecutive hyperedges are disjoint. This study examines the possible k-uniform loose cyclic hypergraphs in relation to the number of vertices and develops a computational procedure for determining their spectral properties. For a loose cyclic hypergraph H = (V, E) with n vertices and m hyperedges, the relation n = m (k-1) is used to describe admissible configurations. An adjacency matrix is formed by assigning each off-diagonal entry according to the number of hyperedges containing the corresponding pair of vertices. A Python-based procedure is then used to construct the adjacency matrix for admissible parameter choices and to compute its eigenvalues and eigenvectors. The method is illustrated using a 4-uniform loose cyclic hypergraph on 15 vertices with five hyperedges. The resulting 15 × 15 adjacency matrix and its eigenvalues demonstrate the computational implementation of the procedure. The study provides a systematic matrix-based approach for obtaining the eigen spectrum of uniform loose cyclic hypergraphs when closed-form expressions are difficult to derive, while retaining the structural conditions that define the loose cyclic arrangement.
Linear hypergraph set-indexers (LHSIs) associate a graph with a vertex hypergraph and an induced edge hypergraph through injective set-valuations and symmetric-difference edge labels. This study examines structural properties of graphs under LHSIs, with particular emphasis on conditions under which the associated verte...
Viji Paul, Saneesh Babu· Asian Research Journal of Ma...· 0 citations
Hypergraphs describe higher-order interactions that involve more than a pair of nodes. A characteristic feature of hypergraphs is that their robustness can be strongly affected by the different roles of the nodes. Indeed, some nodes might be essential for a hyperedge's function, while others might not be. The loss of a...
We study hyperedge partial duals of finite hypermaps in a purely combinatorial framework, without assuming orientability. A hypermap is represented by three fixed-point-free involutions $(\tau_0,\tau_1,\tau_2)$ on its flag set. We first give an explicit construction of the medial map from this model: $02$-orbits become...
Hypergraphs provide a natural framework for modeling higher-order relationships, but the development of spectral techniques with provable guarantees for general non-uniform hypergraphs remains challenging. Building on Banerjee's normalized adjacency matrix and Spiro's averaging-based diffusion framework, we develop a s...
Let G be a k-uniform hypergraph with k ⩾ 2 and 0 ⩽ α < 1. The α-spectral radius of G is the largest modulus of all the eigenvalues of Aα (G), where Aα (G) = αD(G) + (1-α)A(G) is the convex linear combination of D(G) and A(G) with D(G) and A(G) being the degree diagonal tensor and the adjacency tensor of G, respectively...
A randomized algorithm is given that returns a $(1+\varepsilon)-approximation to $m$ with high probability, making $O(\varepsilon^{-2}\sqrt{n} + \sqrt{n}\log n)$ queries, and it is proved that $\Omega(\sqrt{n})$ queries are necessary for any algorithm that obtains a constant factor approximation to $m$.
Deeparnab Chakrabarty, Cooper LaPorte, C. Seshadhri· 0 citations
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