With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Learning on Graphs has attracted immense attention due to its wide real-world applications.
The most popular pipeline for learning on graphs with textual node attributes primarily relies …

In this era of the Internet of Everything (IoE), edge computing has emerged as the critical
enabling technology to solve a series of issues caused by an increasing amount of …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …

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

The nature of heterophilous graphs is significantly different from that of homophilous graphs,
which causes difficulties in early graph neural network (GNN) models and suggests …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …