Download PDF Join the Discussion View in the ACM Digital Library EXAMPLE 2. A standard way of representing graphs is by their adjacency matrices; once we have an adjacency matrix we can obtain a {0, 1 ...

A holy grail of theoretical computer science, with numerous fundamental implications to more applied areas of computing such as operations research and artificial intelligence, is the question of ...

I Program of Production Engineering, COPPE/UFRJ, Brazil. Post-doctoral researcher. E-mail: [email protected] II Universidade Federal do Rio de Janeiro/COPPE, Brazil. Full Professor, retired; Program ...

The eccentric connectivity index based on degree and eccentricity of the vertices of a graph is a widely used graph invariant in mathematics. In this paper we present the explicit generalized ...

Abstract: Graph learning has been extensively investigated for over a decade, in which the graph structure can be learnt from multiple graph signals (e.g., graphical Lasso) or node features (e.g., ...

General graph neural networks (GNNs) implement convolution operations on graphs based on polynomial spectral filters. Existing filters with high-order polynomial approximations can detect more ...

The eccentric connectivity index based on degree and eccentricity of the vertices of a graph is a widely used graph invariant in mathematics. In this paper we present the explicit generalized ...

Designing a genetic algorithm to approximate to a polynomial function specified by the user. This was part of the coursework for ME40212 Biomimetics at the University of Bath, which involved a ...

The original code is as old as from 1995 and was written by Gerhard Krucker (see http://www.krucker.ch/skripten-uebungen/IAMSkript/IAMKap3.pdf#page=14). So all ...

I can find the best-fit polynomial function for the array, y = ax^2 + bx + c (where y = voltage output and x = incident temperature), and if I have arrays of data at flat fields captured at known T0, ...