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We develop a general diagrammatic theory of welded graphs, and provide an extension of Satoh's Tube map from welded graphs to ribbon surface-links. As a topological application, we obtain a complete link-homotopy classification of so-called…

Geometric Topology · Mathematics 2025-07-29 Benjamin Audoux , Jean-Baptiste Meilhan , Akira Yasuhara

We construct graph-valued analogues of the Kuperberg sl(3) and G2 invariants for virtual knots. The restriction of the sl(3) or G2 invariants for classical knots coincides with the usual Homflypt sl(3) invariant and G2 invariants. For…

Geometric Topology · Mathematics 2014-07-11 Louis Hirsch Kauffman , Vassily Olegovich Manturov

The ring of graph invariants is spanned by the basic graph invariants which calculate the number of subgraphs isomorphic to a given graph in other graphs. These subgraphs counting invariants are not algebraically independent. In our view…

Combinatorics · Mathematics 2008-12-11 Tomi Mikkonen

Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph data are often in irregular form, which makes them more…

Machine Learning · Computer Science 2020-06-03 Fenxiao Chen , Yuncheng Wang , Bin Wang , C. -C. Jay Kuo

Diagrams as a graphic expresion of derivatives is proposed for calculation of derivatives for composed function. The concret diagram is understood as a virtual derivative in contrast of concret derivative. In polynomial expression of…

General Mathematics · Mathematics 2011-04-13 Gintaras Valiukevicius

We define a range of new coarse geometric invariants based on various graph-theoretic measures of complexity for finite graphs, including: treewidth, pathwidth, cutwidth and bandwidth. We prove that, for bounded degree graphs, these…

Metric Geometry · Mathematics 2025-08-07 Wanying Huang , David Hume , Samuel J. Kelly , Ryan Lam

Dynamic graph learning has gained significant attention as it offers a powerful means to model intricate interactions among entities across various real-world and scientific domains. Notably, graphs serve as effective representations for…

Machine Learning · Computer Science 2024-01-17 Sanaz Hasanzadeh Fard

Due to their flexibility to represent almost any kind of relational data, graph-based models have enjoyed a tremendous success over the past decades. While graphs are inherently only combinatorial objects, however, many prominent analysis…

Social and Information Networks · Computer Science 2024-03-27 Michaël Fanuel , Antoine Aspeel , Michael T. Schaub , Jean-Charles Delvenne

Classical knot theory deals with {\em diagrams} and {\em invariants}. By means of horizontal {\em trisecants}, we construct a new theory of classical braids with invariants valued in {\em pictures}. These pictures are closely related to…

Geometric Topology · Mathematics 2015-01-22 Vassily Olegovich Manturov

We represent stationary descendant Gromov-Witten invariants of projective space, up to explicit combinatorial factors, by polynomials. One application gives the asymptotic behaviour of large degree behaviour of stationary descendant…

Algebraic Geometry · Mathematics 2012-01-19 Paul Norbury

In modern mathematics, graphs figure as one of the better-investigated class of mathematical objects. Various properties of graphs, as well as graph-processing algorithms, can be useful if graphs of a certain kind are used as denotations…

Logic in Computer Science · Computer Science 2007-05-23 Alex Shkotin

Graph-based methods have been proposed as a unified framework for discrete calculus of local and nonlocal image processing methods in the recent years. In order to translate variational models and partial differential equations to a graph,…

Numerical Analysis · Mathematics 2018-12-10 Ronny Bergmann , Daniel Tenbrinck

When the theory of Leavitt path algebras was already quite advanced, it was discovered that some of the more difficult questions were susceptible to a new approach using topological groupoids. The main result that makes this possible is…

Rings and Algebras · Mathematics 2019-05-16 Simon W. Rigby

We strengthen and put in a broader perspective previous results of the first two authors on colliding permutations. The key to the present approach is a new non-asymptotic invariant for graphs.

Combinatorics · Mathematics 2007-09-28 János Körner , Claudia Malvenuto , Gábor Simonyi

Graph polynomials encode fundamental combinatorial invariants of graphs. Their computation is investigated using tree and path decomposition frameworks, with formal definitions of treewidth, k-trees, and pathwidth establishing the…

Discrete Mathematics · Computer Science 2025-09-29 Mehul Bafna , Shaghik Amirian

We consider knot theories possessing a {\em parity}: each crossing is decreed {\em odd} or {\em even} according to some universal rule. If this rule satisfies some simple axioms concerning the behaviour under Reidemeister moves, this leads…

Geometric Topology · Mathematics 2009-12-31 Vassily Olegovich Manturov

Topology identification and inference of processes evolving over graphs arise in timely applications involving brain, transportation, financial, power, as well as social and information networks. This chapter provides an overview of graph…

Signal Processing · Electrical Eng. & Systems 2025-12-12 Gonzalo Mateos , Yanning Shen , Georgios B. Giannakis , Ananthram Swami

The entanglement of open curves in 3-space appears in many physical systems and affects their material properties and function. A new framework in knot theory was introduced recently, that enables to characterize the complexity of…

Geometric Topology · Mathematics 2023-10-18 Kasturi Barkataki , Louis H. Kauffman , Eleni Panagiotou

Geometric graphs are a special kind of graph with geometric features, which are vital to model many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical symmetries of translations, rotations, and reflections,…

Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases, e.g., hierarchical structures benefit from hyperbolic…

Machine Learning · Computer Science 2020-06-09 Calin Cruceru , Gary Bécigneul , Octavian-Eugen Ganea