With the urgent demand for generalized deep models, many pre-trained big models are
proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT) …

Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Graph Attention Networks (GATs) are one of the most popular GNN architectures and are
considered as the state-of-the-art architecture for representation learning with graphs. In …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Knowledge graph (KG) embedding aims to study the embedding representation to retain the
inherent structure of KGs. Graph neural networks (GNNs), as an effective graph …

Knowledge graph (KG) embeddings learn low-dimensional representations of entities and
relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

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 …

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …