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We develop a general theory of extensions of flat functors along geometric morphisms of toposes, and apply it to the study of the class of theories whose classifying topos is equivalent to a presheaf topos. As a result, we obtain a…

Category Theory · Mathematics 2014-06-23 Olivia Caramello

This is a simple mathematical introduction into Feynman diagram technique, which is a standard physical tool to write perturbative expansions of path integrals near a critical point of the action. I start from a rigorous treatment of a…

Geometric Topology · Mathematics 2011-09-15 Michael Polyak

We develop a theory of confluence of graphs. We describe an algorithm for proving that a given system of reduction rules for abstract graphs and graphs in surfaces is locally confluent. We apply this algorithm to show that each simple Lie…

Quantum Algebra · Mathematics 2014-10-01 Adam S. Sikora , Bruce W. Westbury

This paper is an introduction to the language of Feynman Diagrams. We use Reshetikhin-Turaev graphical calculus to define Feynman diagrams and prove that asymptotic expansions of Gaussian integrals can be written as a sum over a suitable…

Quantum Algebra · Mathematics 2013-09-30 Domenico Fiorenza , Riccardo Murri

The graph reconstruction conjecture asserts that every simple graph on at least three vertices is uniquely determined by its deck of vertex-deleted subgraphs. In this expository article we survey the conjecture and present an…

Combinatorics · Mathematics 2026-04-21 Emilie Dufresne , Gabriela Jeronimo , Jenny Kenkel , Haydee Lindo , Nelly Villamizar

In this paper, we deal with a notion of Banach space-valued mappings defined on a set consisting of finite graphs with uniformly bounded vertex degree. These functions will be endowed with certain boundedness and additivity criteria. We…

Combinatorics · Mathematics 2015-01-14 Felix Pogorzelski

The need for modification of the Huygens-Fresnel superposition principle arises even in the description of the free fields of massive particles and, more extensively, in nonlinear field theories. A wide range of formulations and…

High Energy Physics - Theory · Physics 2017-06-08 M. I. Krivoruchenko

A T-dualized selfdual inspired formulation of massive vector fields coupled to arbitrary matter is generated; subsequently its perturbative series modeling a spontaneously broken gauge theory is analyzed. The new Feynman rules and external…

High Energy Physics - Theory · Physics 2009-11-07 Gordon Chalmers , Warren Siegel

While lifting map has significantly enhanced the expressivity of graph neural networks, extending this paradigm to hypergraphs remains fragmented. To address this, we introduce the categorical Weisfeiler-Lehman framework, which formalizes…

Machine Learning · Computer Science 2026-02-09 Seongjin Choi , Gahee Kim , Se-Young Yun

An analog of Kreimer's coproduct from renormalization of Feynman integrals in quantum field theory, endows an analog of Kontsevich's graph complex with a dg-coalgebra structure. The graph complex is generated by orientation classes of…

Quantum Algebra · Mathematics 2007-05-23 Lucian M. Ionescu

The supergraph technique for calculations in supersymmetric gauge theories where supersymmetry is broken in a "soft" way (without introducing quadratic divergencies) is reviewed. By introducing an external spurion field the set of Feynman…

High Energy Physics - Phenomenology · Physics 2009-10-30 L. V. Avdeev , D. I. Kazakov , I. N. Kondrashuk

The task of constructing compositional semantics for network-style diagrammatic languages, such as electrical circuits or chemical reaction networks, has been dubbed the black boxing problem, as it gives semantics that describes the…

Category Theory · Mathematics 2020-08-24 Brendan Fong , Maru Sarazola

Feynman integrals obey linear relations governed by intersection numbers, which act as scalar products between vector spaces. We present a general algorithm for constructing multivariate intersection numbers relevant to Feynman integrals,…

High Energy Physics - Theory · Physics 2019-11-20 Hjalte Frellesvig , Federico Gasparotto , Manoj K. Mandal , Pierpaolo Mastrolia , Luca Mattiazzi , Sebastian Mizera

Network representation learning and node classification in graphs got significant attention due to the invent of different types graph neural networks. Graph convolution network (GCN) is a popular semi-supervised technique which aggregates…

Social and Information Networks · Computer Science 2020-02-11 Sambaran Bandyopadhyay , Kishalay Das , M. Narasimha Murty

The purpose of this paper is to show that, under certain combinatorial conditions on the graph, parametric Feynman integrals can be realized as periods on the complement of the determinant hypersurface in an affine space depending on the…

Algebraic Geometry · Mathematics 2012-04-11 Paolo Aluffi , Matilde Marcolli

We construct an embedding G of the category of graphs into the category of abelian groups such that for graphs X and Y we have Hom(GX,GY)=Z[Hom(X,Y)], the free abelian group whose basis is the set Hom(X,Y). The isomorphism is functorial in…

Category Theory · Mathematics 2014-03-20 Adam J. Przezdziecki

In recommender systems, user-item interactions can be modeled as a bipartite graph, where user and item nodes are connected by undirected edges. This graph-based view has motivated the rapid adoption of graph neural networks (GNNs), which…

For self-similar sets $X,Y\subseteq \mathbb{R}$, we obtain new results towards the affine embeddings conjecture of Feng-Huang-Rao (2014), and the equivalent weak intersections conjecture. We show that the conjecture holds when the defining…

Dynamical Systems · Mathematics 2024-10-28 Amir Algom , Michael Hochman , Meng Wu

The problem of efficiently characterizing degree sequences of simple hypergraphs is a fundamental long-standing open problem in Graph Theory. Several results are known for restricted versions of this problem. This paper adds to the list of…

Discrete Mathematics · Computer Science 2017-05-02 Syed Mohammad Meesum

Hypergraph neural networks (HGNNs) effectively model higher-order interactions in many real-world systems but remain difficult to interpret, limiting their deployment in high-stakes settings. We introduce CF-HyperGNNExplainer, a…

Machine Learning · Computer Science 2026-05-26 Fabiano Veglianti , Lorenzo Antonelli , Gabriele Tolomei
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