Communication networks are important infrastructures in contemporary society. There are
still many challenges that are not fully solved and new solutions are proposed continuously …

As one of the key communication scenarios in the fifth-generation and also the sixth-
generation (6G) mobile communication networks, ultrareliable and low-latency …

Recent studies show that the predictive performance of graph neural networks (GNNs) is
inconsistent and varies across different experimental runs, even with identical parameters …

Recently, graph neural networks (GNN) were shown to be successful in effectively
representing graph structured data because of their good performance and generalization …

The second volume of “Advancing Uncertain Combinatorics through Graphization,
Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond” …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Effective communication is key to successful, decentralized, multi-robot path planning. Yet, it
is far from obvious what information is crucial to the task at hand, and how and when it must …

Graph neural networks (GNN) have shown great advantages in many graph-based learning
tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif …

An intuitive and accessible text explaining the fundamentals and applications of graph
signal processing. Requiring only an elementary understanding of linear algebra, it covers …

We consider the problem of optimally allocating resources across a set of transmitters and
receivers in a wireless network. The resulting optimization problem takes the form of …