Decision-focused learning (DFL) combines both machine learning and combinatorial
optimization so as to enhance the quality of decision-making. In general, DFL adds an …

This paper provides a comprehensive survey of the integration of graph neural networks
(GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions …

At present, the traffic in wireless sensor networks (WSN) is growing at an extremely fast
speed, consuming more and more network resources. This undoubtedly affects the …

The graph structure is one of the critical data structures in the real world, and its applications
focus on graphs, where scholars study entity features and interactions among various …

Connection management is an important problem for any wireless network to ensure smooth
and well-balanced operation throughout. Traditional methods for connection management …

Collaborative filtering algorithms based on graph neural networks have achieved good
performance in many scenarios of recommender systems (RSs), most of which utilize the …

Generally speaking, artificial intelligent (AI) models are trained under special learning
hypotheses, especially the one that statistics of the training data are static during the training …

The graph neural network (GNN) has demonstrated its superior performance in various
applications. The working mechanism behind it, however, remains mysterious. GNN models …

Graph neural networks (GNNs) aim to learn well-trained representations in a lower-
dimension space for downstream tasks while preserving the topological structures. In recent …

With the increasing adoption of graph neural networks (GNNs) in the graph-based deep
learning community, various graph programming frameworks and models have been …