Conventional graph neural networks (GNNs) are often confronted with fairness issues that may stem from their input, including node attributes and neighbors surrounding a node. While several recent approaches have been proposed to eliminate the bias rooted in sensitive attributes, they ignore other key input of GNNs, namely node, which can introduce since GNNs hinge on neighborhood structures ge...

Inspired by the generalized entropies for graphs, a class of generalized degree-based graph entropies is proposed using the known information-theoretic measures to characterize the structure of complex networks. The new entropies depend on assigning a probability distribution about the degrees to a network. In this paper, some extremal properties of the generalized degree-based graph entropies ...

We consider statistical graph signal processing (GSP) in a generalized framework where each vertex of is associated with an element from Hilbert space. This general model encompasses various signals such as the traditional scalar-valued signal, multichannel and discrete- continuous-time signals, allowing us to build unified theory random processes. introduce notion joint wide-sense stationarity...