PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters This repository contains a PyTorch implementation of ICLR 2024 paper " PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable ...

Chebyshev interpolation polynomials exhibit the exponential approximation property to analytic functions on a cube. Based on the Chebyshev interpolation polynomial approximation, we propose iterative ...

Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the ...

Recently, a research team from Hokkaido University made significant progress in the complexity study of the Hitting Geodesic Intervals (HGI) problem. The HGI problem aims to find a small set of ...

Includes the basic convolutional graph neural networks (selection -zero-padding and graph coarsening-, spectral, aggregation), and some non-convolutional graph neural networks as well (node-variant, ...

Moreover, we propose a close relationship between independence polynomial of corona graph and h h -polynomial of its independence simplicial complexes. Thereby, the formula of h h -polynomial is ...

The graph polynomial of a Feynman diagram is defined in terms of the spanning trees and forests of the underlying graph. The associated Feynman integral can be expressed as a Mellin transform of a ...

The associated Feynman integral can be expressed as a Mellin transform of a power of this graph polynomial, interpreted as a function of its coefficients.

CBSE Class 10 Assertion Reasoning Questions 2025 for Maths, Science, Social Science along with solution. Download Free PDF ...

To support their approach, the authors draw on the rich machinery of algebraic geometry—the study of shapes defined by polynomial equations. They link scattering amplitudes to “graph polynomials,” ...

Top 50 Class 10 Maths MCQs for CBSE Half Yearly Exam 2025. Practice multiple-choice questions with answers to boost your exam prepration ...