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Related papers: Bratteli--Vershik models and graph covering models

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Once the set of finite graphs is equipped with an algebra structure (arising from the definition of operations that generalize the concatenation of words), one can define the notion of a recognizable set of graphs in terms of finite…

Logic in Computer Science · Computer Science 2007-05-23 Bruno Courcelle , Pascal Weil

We consider the dynamics of relativistic spin-half particles in quantum graphs with transparent branching points. The system is modeled by combining the quantum graph concept with the one of transparent boundary conditions applied to the…

Quantum Physics · Physics 2020-07-01 J. R. Yusupov , K. K. Sabirov , Q. U. Asadov , M. Ehrhardt , D. U. Matrasulov

The main goal of this paper is proving the fixed point theorem for finite groups acting on weakly systolic complexes. As corollaries we obtain results concerning classifying spaces for the family of finite subgroups of weakly systolic…

Group Theory · Mathematics 2012-09-26 Victor Chepoi , Damian Osajda

This article proposes methods to model nonstationary temporal graph processes. This corresponds to modelling the observation of edge variables (relationships between objects) indicating interactions between pairs of nodes (or objects)…

Methodology · Statistics 2022-07-07 Maria Suveges , Sofia C. Olhede

We investigate topological, combinatorial, statistical, and enumeration properties of finite graphs with high Kolmogorov complexity (almost all graphs) using the novel incompressibility method. Example results are: (i) the mean and variance…

Combinatorics · Mathematics 2007-05-23 Harry Buhrman , Ming Li , John Tromp , Paul Vitanyi

In this paper we study the dimension spectrum of general conformal graph directed Markov systems modeled by countable state symbolic subshifts of finite type. We perform a comprehensive study of the dimension spectrum addressing questions…

Dynamical Systems · Mathematics 2019-09-16 Vasileios Chousionis , Dmitriy Leykekhman , Mariusz Urbański

Learning useful representations is a key ingredient to the success of modern machine learning. Currently, representation learning mostly relies on embedding data into Euclidean space. However, recent work has shown that data in some domains…

Machine Learning · Computer Science 2019-10-17 Denis Mazur , Vage Egiazarian , Stanislav Morozov , Artem Babenko

One of the principal goals of graph modeling is to capture the building blocks of network data in order to study various physical and natural phenomena. Recent work at the intersection of formal language theory and graph theory has explored…

Social and Information Networks · Computer Science 2023-01-30 Satyaki Sikdar , Justus Hibshman , Tim Weninger

A one dimensional lattice model is formulated to study tapping dynamics and the long time steady distribution in granular media. The dynamics conserves the number of particles in the system, and density changes are associated to the…

Statistical Mechanics · Physics 2007-05-23 J. J. Brey , A. Prados

Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful…

Machine Learning · Computer Science 2018-03-12 Yujia Li , Oriol Vinyals , Chris Dyer , Razvan Pascanu , Peter Battaglia

The Weingarten lattice gauge model of Nambu-Goto strings is generalised to allow for fluctuations of an intrinsic worldsheet metric through a dynamical quadrilation. The continuum limit is taken for $c\leq1$ matter, reproducing the results…

High Energy Physics - Theory · Physics 2015-06-26 S. Dalley

Iterated Graph Systems (IGS) transplant ideas from fractal geometry into graph theory. Building on this framework, we extend Edge IGS from the primitive to the reducible setting. Within this broader context, we formulate rigorous…

Combinatorics · Mathematics 2026-05-13 Nero Ziyu Li , Frank Xin Hu , Thomas Britz

The purpose of the paper is a general analysis of path space measures. Our focus is a certain path space analysis on generalized Bratteli diagrams. We use this in a systematic study of systems of self-similar measures (the term ``IFS…

Dynamical Systems · Mathematics 2022-10-26 Sergey Bezuglyi , Palle E. T. Jorgensen

We present an approach to reduced-order modelling that builds off recent graph-theoretic work for representation, exploration, and analysis of computed states of physical systems (Banerjee et al., Comp. Meth. App. Mech. Eng., 351, 501-530,…

Numerical Analysis · Mathematics 2022-12-21 Matthew Duschenes , Siddhartha Srivastava , Krishna Garikipati

Applying a method of Godsil and McKay \cite{GM} to some graphs related to the symplectic graph, a series of new infinite families of strongly regular graphs with parameters…

Combinatorics · Mathematics 2016-05-25 Alice M. W. Hui , Bernardo Rodrigues

This paper completes the comprehensive study of the dimer model on infinite minimal graphs with Fock's weights [arXiv:1503.00289] initiated in [arXiv:2007.14699]: the latter article dealt with the elliptic case, i.e., models whose…

Probability · Mathematics 2023-04-05 Cédric Boutillier , David Cimasoni , Béatrice de Tilière

In this note we answer a question of Johannes Carmesin, which was circulated at the Oberwolfach Workshop on "Graph Theory" in January 2025. We provide a unimodular, locally finite, and vertex-transitive graph without any perfect finite…

Combinatorics · Mathematics 2025-02-11 Andreas Thom

We propose and investigate general kinetic models %of Boltzmann type with transition probabilities that can describe the simultaneous change of multiple microscopic states of the interacting agents. These models can be applied to many…

Mathematical Physics · Physics 2023-02-23 Marzia Bisi , Nadia Loy

We consider several new classes of viable vector field alternatives to the inflaton and quintessence scalar fields. Spatial vector fields are shown to be compatible with the cosmological anisotropy bounds if only slightly displaced from the…

Astrophysics · Physics 2010-11-19 Tomi S. Koivisto , David F. Mota

Graph representation learning (also called graph embeddings) is a popular technique for incorporating network structure into machine learning models. Unsupervised graph embedding methods aim to capture graph structure by learning a…

Social and Information Networks · Computer Science 2022-01-24 Andrew Stolman , Caleb Levy , C. Seshadhri , Aneesh Sharma