Simonovits's theorem in random graphs
Abstract Let H$H$ be a graph with χ(H)=r+1$\chi (H) = r+1$. Simonovits's theorem states that, if H$H$ is edge‐critical, the unique largest H$H$‐free subgraph of Kn$K_n$ is its largest r$r$‐partite subgraph, provided that n$n$ is sufficiently large. We show that the same holds with Kn$K_n$ replaced by the binomial random graph Gn,p$G_{n,p}$ whenever H$H$
Ilay Hoshen, Wojciech Samotij
wiley +1 more source
Effects on Seidel energy of two special types of graphs by perturbing edges
Let G be a simple undirected graph, and let S(G) be its Seidel matrix. The Seidel energy of G is defined as ES(G)=∑i=1n|λS(G)|, where λS(G),λS(G),…,λS(G) are Seidel eigenvalues of G.
doaj +1 more source
Spectral energies of redefined Zagreb indices and comparative QSPR applications to anticancer and alcohol datasets. [PDF]
Zeren Y, Çiçek E, Alsharafi M.
europepmc +1 more source
Informative Missingness in Nominal Data: A Graph-Theoretic Approach to Revealing Hidden Structure. [PDF]
Zangene E, Schwämmle V, Jafari M.
europepmc +1 more source
A generative model for bipartite gene-sharing networks. [PDF]
Iranzo J +4 more
europepmc +1 more source
First Zagreb energy of self-looped graphs: predictive insights into kidney infection drugs and theoretical bounds. [PDF]
Yashaswini L, Rakshith BR, Nagaraja P.
europepmc +1 more source
Polyhedral Graphs of GRAPH PARTITIONING and COMPLETE BIPARTITE SUBGRAPH Problems
We provide an effective description of graphs of polyhedra for GRAPH PARTITIONING and COMPLETE BIPARTITE SUBGRAPH problems. We establish the fact, that the clique number for each of this problems increases exponentially with the dimension of the space.
V. A. Bondarenko, A. I. Antonov
core
Hyperpathway: visualizing organization of pathway-molecule enriched interactions in omics studies via hyperbolic bipartite network embedding. [PDF]
Abdelhamid I +5 more
europepmc +1 more source
Prodeg type Shannon graph entropies with closed forms bounds and QSPR modeling. [PDF]
Alsharafi M, Zeren Y.
europepmc +1 more source
DynaTCR: dynamic hard-negative ensemble graph learning improves TCR-epitope binding prediction. [PDF]
Fu X +5 more
europepmc +1 more source

