Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …

Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

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

Abstract Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and
regression tasks in Big Data. Researchers should be familiar with the strengths and …

We present network embedding algorithms that capture information about a node from the
local distribution over node attributes around it, as observed over random walks following an …

Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Graph neural network, as a powerful graph representation technique based on deep
learning, has shown superior performance and attracted considerable research interest …

This book is a foundational guide to graph representation learning, including state-of-the art
advances, and introduces the highly successful graph neural network (GNN) formalism …

Graph representation learning resurges as a trending research subject owing to the
widespread use of deep learning for Euclidean data, which inspire various creative designs …