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Branch points of a real 2-surface S in a 4-manifold M generalize the branch points of complex curves in complex surfaces: for example, they can occur as singularities of minimal surfaces. We investigate such a branch point p when S is…

Differential Geometry · Mathematics 2007-05-23 Marina Ville

Critical nets in $\mathbb{R}^k$ (sometimes called geodesic nets) are embedded graph with the property that their embedding is a critical point of the total (edge) length functional and under the constraint that certain 1-valent vertices…

Differential Geometry · Mathematics 2021-01-05 Antoine Gournay , Yashar Memarian

Random graphs have played an instrumental role in modelling real-world networks arising from the internet topology, social networks, or even protein-interaction networks within cells. Percolation, on the other hand, has been the fundamental…

Probability · Mathematics 2018-09-12 Souvik Dhara

Stable fold maps are fundamental tools in a generalization of the theory of Morse functions on smooth manifolds and its application to studies of topological properties of smooth manifolds. Round fold maps were introduced as stable fold…

General Topology · Mathematics 2014-12-16 Naoki Kitazawa

The language of graph theory, or network science, has proven to be an exceptional tool for addressing myriad problems in neuroscience. Yet, the use of networks is predicated on a critical simplifying assumption: that the quintessential unit…

Neurons and Cognition · Quantitative Biology 2016-01-14 Chad Giusti , Robert Ghrist , Danielle S. Bassett

We obtain an analog of the prime number theorem for a class of branched covering maps on the $2$-sphere $S^2$ called expanding Thurston maps, which are topological models of some non-uniformly expanding rational maps without any smoothness…

Dynamical Systems · Mathematics 2024-04-11 Zhiqiang Li , Tianyi Zheng

We classify simple heteroclinic networks for a $\Gamma$-equivariant system in ${\mathbb R}^4$ with finite $\Gamma \subset {\rm O}(4)$, proceeding as follows: we define a graph associated with a given $\Gamma \subset {\rm O}(n)$ and identify…

Dynamical Systems · Mathematics 2019-09-04 Olga Podvigina , Alexander Lohse

We introduce a novel architecture for graph networks which is equivariant to any transformation in the coordinate embeddings that preserves the distance between neighbouring nodes. In particular, it is equivariant to the Euclidean and…

Machine Learning · Computer Science 2021-07-13 Francesco Farina , Emma Slade

We investigate a network model based on an infinite regular square lattice embedded in the Euclidean plane where the node connection probability is given by the geometrical distance of nodes. We show that the degree distribution in the…

Physics and Society · Physics 2008-06-23 Matus Medo , Jan Smrek

A simple topological graph is a topological graph in which any two edges have at most one common point, which is either their common endpoint or a proper crossing. More generally, in a k-simple topological graph, every pair of edges has at…

Computational Geometry · Computer Science 2016-02-22 Péter Hajnal , Alexander Igamberdiev , Günter Rote , André Schulz

Can we model Non-Euclidean graphs as pure language or even Euclidean vectors while retaining their inherent information? The Non-Euclidean property have posed a long term challenge in graph modeling. Despite recent graph neural networks and…

Machine Learning · Computer Science 2024-05-30 Zhangyang Gao , Daize Dong , Cheng Tan , Jun Xia , Bozhen Hu , Stan Z. Li

Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks…

Machine Learning · Computer Science 2021-10-07 Maosheng Yang , Elvin Isufi , Geert Leus

The coarsest approximation of the structure of a complex network, such as the Internet, is a simple undirected unweighted graph. This approximation, however, loses too much detail. In reality, objects represented by vertices and edges in…

Networking and Internet Architecture · Computer Science 2009-11-02 Xenofontas Dimitropoulos , Dmitri Krioukov , Amin Vahdat , George Riley

Networks of disparate phenomena-- be it the global ecology, human social institutions, within the human brain, or in micro-scale protein interactions-- exhibit broadly consistent architectural features. To explain this, we propose a new…

Physics and Society · Physics 2021-01-26 Keith M. Smith

The study of complex networks is a significant development in modern science, and has enriched the social sciences, biology, physics, and computer science. Models and algorithms for such networks are pervasive in our society, and impact…

Machine Learning · Computer Science 2022-06-08 C. Seshadhri , Aneesh Sharma , Andrew Stolman , Ashish Goel

Among Thurston maps (orientation-preserving, postcritically finite branched coverings of the 2-sphere to itself), those that arise as subdivision maps of a finite subdivision rule form a special family. For such maps, we investigate…

Dynamical Systems · Mathematics 2015-08-04 William J. Floyd , Walter R. Parry , Kevin M. Pilgrim

Phylogenetic networks generalise phylogenetic trees and allow for the accurate representation of the evolutionary history of a set of present-day species whose past includes reticulate events such as hybridisation and lateral gene transfer.…

Populations and Evolution · Quantitative Biology 2018-09-05 Joan Carles Pons , Charles Semple , Mike Steel

In this paper we prove existence of matings between a large class of renormalizable cubic polynomials with one fixed critical point and another cubic polynomial having two fixed critical points. The resulting mating is a Newton map. Our…

Dynamical Systems · Mathematics 2018-05-16 Magnus Aspenberg , Pascale Roesch

Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\mathbb{R}^d$. Ideally, this mapping is such that `similar' nodes are mapped onto nearby points, such that the NE can be used for purposes such…

Machine Learning · Statistics 2018-10-17 Bo Kang , Jefrey Lijffijt , Tijl De Bie

Group symmetry is inherent in a wide variety of data distributions. Data processing that preserves symmetry is described as an equivariant map and often effective in achieving high performance. Convolutional neural networks (CNNs) have been…

Machine Learning · Statistics 2020-12-29 Wataru Kumagai , Akiyoshi Sannai
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