We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning.
DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop …

In recent years, Dynamic Graph (DG) representations have been increasingly used for
modeling dynamic systems due to their ability to integrate both topological and temporal …

We present RelBench, a public benchmark for solving predictive tasks over relational
databases with graph neural networks. RelBench provides databases and tasks spanning …

Graphs have been widely used in the past decades of big data and AI to model
comprehensive relational data. When analyzing a graph's statistical properties, graph laws …

To handle graphs in which features or connections are evolving over time, a series of
temporal graph neural networks (TGNNs) have been proposed. Despite the success of …

In Web3 social platforms, ie social web applications that rely on blockchain technology to
support their functionalities, interactions among users are usually multimodal, from common …

Many real-world networks evolve over time, and predicting the evolution of such networks
remains a challenging task. Graph Neural Networks (GNNs) have shown empirical success …

The global economy relies on the flow of goods over supply chain networks, with nodes as
firms and edges as transactions between firms. While we may observe these external …

Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling
accurate social recommendation (link prediction) or early detection of cancer cells …

Over the past two decades, there has been a tremendous increase in the growth of
representation learning methods for graphs, with numerous applications across various …