Graphs contain a plethora of valuable information about the underlying data which can be
extracted, analyzed, and visualized using Machine Learning (ML). The challenge is that …

Recent years have witnessed a dramatic increase in graph applications due to
advancements in information and communication technologies. In various applications, such …

Graph representation learning (GRL) has recently drawn a lot of attention due to its
advantage in solving various machine learning tasks on graphs/networks, ranging from drug …

To realize simulation experiments in large-scale Internet of Things (IoT) networks, this work
studies the utilization of deep graph generative models to generate IoT networks, which can …

The challenges posed by the future wireless communication network, which will be a huge
system with more complex structures, diverse functions, and massive communication ends …

Graph Convolutional Network (GCN) is adopted to tackle the problem of convolution
operation in non-Euclidean space. Previous works on GCN have made some progress …

The performance of graph neural networks (GNNs) in a variety of graph-related tasks, such
as node categorization, has been remarkably good. Existing GNN models, especially when …

Graph neural networks (GNNs) can improve the efficiency of learning wireless policies by
leveraging their permutation properties and topology prior. While mismatched permutation …

Graph generation has emerged as a crucial task in machine learning, with significant
challenges in generating graphs that accurately reflect specific properties. Existing methods …

Graph neural networks (GNNs) are widely used on graph-structured data, and its research
has made substantial progress in recent years. However, given the various number of …