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In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological and temporal information in a compact representation. Dynamic graphs allow to…

Machine Learning · Computer Science 2023-04-13 Leshanshui Yang , Sébastien Adam , Clément Chatelain

Many problems can be presented in an abstract form through a wide range of binary objects and relations which are defined over problem domain. In these problems, graphical demonstration of defined binary objects and solutions is the most…

Graphics · Computer Science 2016-03-24 Mohammadreza Ashouri , Ali Golshani , Dara Moazzmi , Mandana Ghasemi

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical…

Machine Learning · Statistics 2012-04-03 Ryan A. Rossi , Luke K. McDowell , David W. Aha , Jennifer Neville

By designating vertices with variables, a simple undirected graph can be augmented to have an associated representing rational function in two variables taking the complex bi-upper halfplane to itself. We give relations between representing…

Complex Variables · Mathematics 2025-11-05 Lily Adlin , Giovani Thai , Samuel Tiscareno , Ryan Tully-Doyle

This article introduces a new approach to discrete curvature based on the concept of effective resistances. We propose a curvature on the nodes and links of a graph and present the evidence for their interpretation as a curvature. Notably,…

Differential Geometry · Mathematics 2022-09-26 Karel Devriendt , Renaud Lambiotte

We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the literature, including Friedman et al. (2007), Liu et al.…

Machine Learning · Statistics 2020-06-29 Tuo Zhao , Han Liu , Kathryn Roeder , John Lafferty , Larry Wasserman

We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from…

Machine Learning · Computer Science 2016-07-11 James Atwood , Don Towsley

In this paper, a function on any pair of graphs is defined whose properties are similar to the properties of dot product in vector space. This function enables us to define graph orthogonality and, also, a new metric on isomorphism classes…

Combinatorics · Mathematics 2018-10-23 Ameneh Farhadian

Order diagrams are an important tool to visualize the complex structure of ordered sets. Favorable drawings of order diagrams, i.e., easily readable for humans, are hard to come by, even for small ordered sets. Many attempts were made to…

Computational Geometry · Computer Science 2023-12-29 Dominik Dürrschnabel , Tom Hanika , Gerd Stumme

The graphical notion of effective resistance has found wide-ranging applications in many areas of pure mathematics, applied mathematics and control theory. By the nature of its construction, effective resistance can only be computed in…

Optimization and Control · Mathematics 2013-10-23 George Forrest Young , Luca Scardovi , Naomi Ehrich Leonard

A directed hypergraph (dihypergraph) consists of a set of vertices and a set of hyperarcs, where each hyperarc is partitioned into a head and a tail. Directed hypergraphs are useful in many applications, including the study of chemical…

Combinatorics · Mathematics 2026-04-14 Catherine Greenhill , Tamás Makai

We present an algorithm to evaluate the large deviation functions associated to history-dependent observables. Instead of relying on a time discretisation procedure to approximate the dynamics, we provide a direct continuous-time algorithm,…

Statistical Mechanics · Physics 2007-12-03 Vivien Lecomte , Julien Tailleur

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

We present in this article the model Function-described graph (FDG), which is a type of compact representation of a set of attributed graphs (AGs) that borrow from Random Graphs the capability of probabilistic modelling of structural and…

Artificial Intelligence · Computer Science 2016-05-11 Francesc Serratosa

Recently, we have proposed a new diffusive representation for fractional derivatives and, based on this representation, suggested an algorithm for their numerical computation. From the construction of the algorithm, it is immediately…

Numerical Analysis · Mathematics 2022-04-12 Kai Diethelm

Data analyses are often constructed in an imperative manner, where commands representing actions taken on the data are issued sequentially. The publication of these commands, along with the data, is essential to the reproducibility of the…

Other Statistics · Statistics 2026-03-12 Roger D. Peng

Nowadays, Web services (WS) remain a main actor in the implementation of distributed applications. They represent a new promising paradigm for the development, deployment and integration of Internet applications. The aim of Web services…

Software Engineering · Computer Science 2013-06-19 Chaker Ben Mahmoud , Fathia Bettahar , Hajer Abderrahim , Houda Saidi

We propose a new approach to text semantic analysis and general corpus analysis using, as termed in this article, a "bi-gram graph" representation of a corpus. The different attributes derived from graph theory are measured and analyzed as…

Machine Learning · Computer Science 2021-07-30 Thomas Konstantinovsky , Matan Mizrachi

What is a mathematically rigorous way to describe the taxi-pickup distribution in Manhattan, or the profile information in online social networks? A deep understanding of representing those data not only provides insights to the data…

Signal Processing · Electrical Eng. & Systems 2018-03-09 Siheng Chen , Aarti Singh , Jelena Kovačević

This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model a directed graph as a finite set of observations from a diffusion on a manifold endowed with a vector field.…

Machine Learning · Statistics 2014-06-03 Dominique Perrault-Joncas , Marina Meila