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 …

Abstract Cyber Threat Intelligence (CTI) provides ample evidence and information regarding
the detection of cyber attack activities. Existing methods employ CTI reports to extract …

Graph neural networks (GNNs) have accomplished great success in learning complex
systems of relations arising in broad problem settings ranging from e-commerce, social …

In recent years, graph neural networks (GNNs)-based methods have been widely adopted
for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich …

Abstract Knowledge Graphs (KGs) are among the most commonly used knowledge
representation paradigms, being at the core of tasks such as question answering or …

Knowledge graph representation learning (KGRL) or knowledge graph embedding (KGE)
plays a crucial role in AI applications for knowledge construction and information …

The attention mechanism enables graph neural networks (GNNs) to learn the attention
weights between the target node and its one-hop neighbors, thereby improving the …

Heterogeneous graph contrastive learning has received wide attention recently. Some
existing methods use meta-paths, which are sequences of object types that capture …

Recently, graph convolutional network (GCN) has become one of the most popular and state-
of-the-art collaborative filtering (CF) methods. Existing GCN-based CF studies have made …

Learning representations from pairwise comparisons has achieved significant success in
various fields, including computer vision and information retrieval. In recommendation …