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Category theory provides a means through which many far-ranging fields of mathematics can be related by their similar structure. In a paper by Robinson [2], this interconnectivity afforded by categorical perspectives allowed for the…

Algebraic Topology · Mathematics 2020-12-03 Karthik Boyareddygari

In most systems, its division into interacting constituent elements gives rise to a natural network structure. Analyzing the dynamics of these elements and the topology of these natural graphs gave rise to the fields of (nonlinear) dynamics…

Classical Physics · Physics 2018-09-12 Sophia R. Sklan , Baowen Li

AThe paper gives a few arguments in favour of the use of chain graphs for description of probabilistic conditional independence structures. Every Bayesian network model can be equivalently introduced by means of a factorization formula with…

Artificial Intelligence · Computer Science 2013-02-01 Milan Studeny

We show that the self-assembly of a diverse collection of building blocks can be understood within a common physical framework. These building blocks, which form periodic honeycomb networks and nonperiodic variants thereof, range in size…

Real networks are complex dynamical systems, evolving over time with the addition and deletion of nodes and links. Currently, there exists no principled mathematical theory for their dynamics -- a grand-challenge open problem. Here, we show…

Physics and Society · Physics 2024-06-18 Evangelos S. Papaefthymiou , Costas Iordanou , Fragkiskos Papadopoulos

Proofs of coherence in category theory, starting from Mac Lane's original proof of coherence for monoidal categories, are sometimes based on confluence techniques analogous to what one finds in the lambda calculus, or in term-rewriting…

Category Theory · Mathematics 2007-05-23 K. Dosen , Z. Petric

Graph Neural Networks (GNNs) have shown great ability in modeling graph-structured data for various domains. However, GNNs are known as black-box models that lack interpretability. Without understanding their inner working, we cannot fully…

Machine Learning · Computer Science 2022-10-06 Enyan Dai , Suhang Wang

A combinatorial code $\mathcal{C}$ is a collection of subsets of $[n]$, or equivalently a set of points in $\{0,1\}^n$. A morphism of codes is a map from one combinatorial code to another such that the coordinates of points in the image can…

Combinatorics · Mathematics 2026-03-12 Juliann Geraci , Alexander B. Kunin , Alexandra Seceleanu

A common feature of biological networks is the geometric property of self-similarity. Molecular regulatory networks through to circulatory systems, nervous systems, social systems and ecological trophic networks, show self-similar…

Molecular Networks · Quantitative Biology 2012-03-09 Simon DeDeo , David C. Krakauer

Conformal nets are a mathematical model for conformal field theory, and defects between conformal nets are a model for an interaction or phase transition between two conformal field theories. In the preceding paper of this series, we…

Category Theory · Mathematics 2019-05-17 Arthur Bartels , Christopher L. Douglas , André Henriques

In this survey, we present the basic facts about conduction in infinite networks. This survey is based on the work of Flanders, Zemanian, and Thomassen, who developed the theory of infinite networks from scratch. Here we show how to get a…

Probability · Mathematics 2007-05-23 Peter G. Doyle

Complex networks are a recent type of frameworks used to study complex systems with many interacting elements, such as Self-Organized Criticality (SOC). The network node's tendency to link to other nodes of similar type is characterized by…

Adaptation and Self-Organizing Systems · Physics 2017-04-14 Alfonso Allen-Perkins , Javier Galeano , Juan Manuel Pastor

How perception and reasoning arise from neuronal network activity is poorly understood. This is reflected in the fundamental limitations of connectionist artificial intelligence, typified by deep neural networks trained via gradient-based…

Artificial Intelligence · Computer Science 2020-02-27 Paul J. Blazek , Milo M. Lin

Transformer-based embedding methods have dominated the field of sentence representation in recent years. Although they have achieved remarkable performance on NLP missions, such as semantic textual similarity (STS) tasks, their black-box…

Computation and Language · Computer Science 2025-05-15 Fei Yang

In this paper, we give precise mathematical form to the idea of a structure whose data and axioms are faithfully represented by a graphical calculus; some prominent examples are operads, polycategories, properads, and PROPs. Building on the…

Logic in Computer Science · Computer Science 2017-10-11 Richard Garner , Tom Hirschowitz

We study homomorphism polynomials, which are polynomials that enumerate all homomorphisms from a pattern graph $H$ to $n$-vertex graphs. These polynomials have received a lot of attention recently for their crucial role in several new…

Computational Complexity · Computer Science 2020-11-17 Balagopal Komarath , Anurag Pandey , C. S. Rahul

The Rooted Maps Theory, a branch of the Theory of Homology, is shown to be a powerful tool for investigating the topological properties of Feynman diagrams, related to the single particle propagator in the quantum many-body systems. The…

Nuclear Theory · Physics 2017-07-13 A. Prunotto , W. M. Alberico , P. Czerski

In this paper we will prove that there exists a covariant functor from the category of schemes to the category of graphs. This functor provides a combination between algebraic varieties and combinatorial graphs so that the invariants…

Algebraic Geometry · Mathematics 2009-07-06 Feng-Wen An

In this article, we characterize convexity in terms of algebras over a PROP, and establish a tensor-product-like symmetric monoidal structure on the category of convex sets. Using these two structures, and the theory of $\scr{O}$-monoidal…

Category Theory · Mathematics 2024-03-28 Redi Haderi , Cihan Okay , Walker H. Stern

Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability…

Machine Learning · Computer Science 2024-11-28 Pirzada Suhail , Hao Tang , Amit Sethi