Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …

Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

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, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

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 …

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 …

Predicting accurate future trajectories of multiple agents is essential for autonomous systems
but is challenging due to the complex interaction between agents and the uncertainty in …

Interpretable graph learning is in need as many scientific applications depend on learning
models to collect insights from graph-structured data. Previous works mostly focused on …