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Related papers: Soft Rough Graphs

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Topological metrics of graphs provide a natural way to describe the prominent features of various types of networks. Graph metrics describe the structure and interplay of graph edges and have found applications in many scientific fields. In…

Data Structures and Algorithms · Computer Science 2018-06-21 Loukianos Spyrou , Javier Escudero

Rough set theory, a mathematical tool to deal with inexact or uncertain knowledge in information systems, has originally described the indiscernibility of elements by equivalence relations. Covering rough sets are a natural extension of…

Artificial Intelligence · Computer Science 2015-03-13 Ping Zhu

Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges. The number of research papers in the literature concerning these models is growing rapidly due to their impressive performance on…

Machine Learning · Computer Science 2024-12-30 James H. Tanis , Chris Giannella , Adrian V. Mariano

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

A new paradigm recently emerged in financial modelling: rough (stochastic) volatility, first observed by Gatheral et al. in high-frequency data, subsequently derived within market microstructure models, also turned out to capture…

Pricing of Securities · Quantitative Finance 2017-10-23 Christian Bayer , Peter K. Friz , Paul Gassiat , Joerg Martin , Benjamin Stemper

While orthogonal drawings have a long history, smooth orthogonal drawings have been introduced only recently. So far, only planar drawings or drawings with an arbitrary number of crossings per edge have been studied. Recently, a lot of…

Machine learning models for graphs in real-world applications are prone to two primary types of uncertainty: (1) those that arise from incomplete and noisy data and (2) those that arise from uncertainty of the model in its output. These…

Machine Learning · Computer Science 2024-12-10 Zohair Shafi , Germans Savcisens , Tina Eliassi-Rad

Random graphs have proven to be one of the most important and fruitful concepts in modern Combinatorics and Theoretical Computer Science. Besides being a fascinating study subject for their own sake, they serve as essential instruments in…

Combinatorics · Mathematics 2007-05-23 Michael Krivelevich , Benny Sudakov

A number of generalizations of stochastic and information-theoretic randomness are known in the literature. However, they are not compatible with handling meaning in vague and dynamic contexts of rough reasoning (and therefore explainable…

Artificial Intelligence · Computer Science 2023-04-04 Mani A

We present an overview of the differential geometry of curves and surfaces using examples from soft matter as illustrations. The presentation requires a background only in vector calculus and is otherwise self-contained.

Soft Condensed Matter · Physics 2009-11-07 Randall D. Kamien

This paper further studies the fuzzy rough sets based on fuzzy coverings. We first present the notions of the lower and upper approximation operators based on fuzzy coverings and derive their basic properties. To facilitate the computation…

Information Theory · Computer Science 2013-04-02 Guangming Lang , Qingguo Li , Lankun Guo

Throttling in graphs optimizes a sum or product of resources used, such as the number of vertices in an initial set, and time required, such as the propagation time, to complete a given task. We introduce a new technique to establish sharp…

Combinatorics · Mathematics 2025-01-15 Ryan Blair , Gabriel Elvin , Veronika Furst , Leslie Hogben , Nandita Sahajpal , Tony W. H. Wong

Notions of core, support and inversion of a soft set have been defined and studied. Soft approximations are soft sets developed through core and support, and are used for granulating the soft space. Membership structure of a soft set has…

Artificial Intelligence · Computer Science 2016-11-25 Athar Kharal

Subgraph densities have been defined, and served as basic tools, both in the case of graphons (limits of dense graph sequences) and graphings (limits of bounded-degree graph sequences). While limit objects have been described for the…

Combinatorics · Mathematics 2021-05-11 Dávid Kunszenti-Kovács , László Lovász , Balázs Szegedy

There has been a great deal of attention recently to graphs whose vertex set is a group, defined using the group structure. (The commuting graph, where two elements are joined if they commute, is the oldest and most famous example.) The…

Combinatorics · Mathematics 2026-02-03 Peter J. Cameron

Smooth parametrization consists in a subdivision of the mathematical objects under consideration into simple pieces, and then parametric representation of each piece, while keeping control of high order derivatives. The main goal of the…

Computational Geometry · Computer Science 2014-07-14 Y. Yomdin

In order to provide a good categorical setting to the many different spaces of fields arising in the description of physical theories, a pedagogical introduction to the categorical notion of smooth sets is provided and some simple…

Mathematical Physics · Physics 2025-10-24 Alberto Ibort , Arnau Mas

The contribution of this paper is to provide a semantic model (using soft constraints) of the words used by web-users to describe objects in a language game; a game in which one user describes a selected object of those composing the scene,…

Computation and Language · Computer Science 2010-05-31 Sergio Guadarrama , David P. Pancho

We present a form of algebraic reasoning for computational objects which are expressed as graphs. Edges describe the flow of data between primitive operations which are represented by vertices. These graphs have an interface made of…

Logic in Computer Science · Computer Science 2010-07-23 Lucas Dixon , Ross Duncan , Aleks Kissinger

Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these methods have been extended in order to deal with uncertainty…

Machine Learning · Computer Science 2018-11-20 Myriam Tami , Marianne Clausel , Emilie Devijver , Adrien Dulac , Eric Gaussier , Stefan Janaqi , Meriam Chebre