The code includes a Polynomial class which allows for defining a polynomial by specifying the coefficents (an values). Learned how to make use of the matplotlib.pyplot class to generate useful images; ...

Graph Neural Networks (GNNs) exploit signals from node features and the input graph topology to improve node classification task performance. However, these models tend to perform poorly on ...

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., graph metric ...

Various aspects of combinatorial information concerning a graph is stored in the coefficients of a specific graph polynomial, so represented also by the roots of such graph polynomial. It is natural ...

We propose a multivariable continuous polynomial optimization formulation to find arbitrary maximal independent sets of any size for any graph. A local optima of the optimization problem yields a ...

Graph Neural Networks (GNNs) exploit signals from node features and the input graph topology to improve node classification task performance. Recently proposed GNNs work across a variety of homophilic ...