Abstract We present the Temporal Graph Benchmark (TGB), a collection of challenging and
diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine …

This paper proposes a Video Graph Transformer (VGT) model for Video Question Answering
(VideoQA). VGT's uniqueness are two-fold: 1) it designs a dynamic graph transformer …

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 …

Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto
methods to extract spatial-temporal information for temporal graph learning. Interestingly, we …

We propose to perform video question answering (VideoQA) in a Co ntrastive manner via a
V ideo G raph T ransformer model (CoVGT). CoVGT's uniqueness and superiority are three …

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 …

Occupational skill demand (OSD) forecasting seeks to predict dynamic skill demand specific
to occupations, beneficial for employees and employers to grasp occupational nature and …

Many graph representation learning (GRL) problems are dynamic, with millions of edges
added or removed per second. A fundamental workload in this setting is dynamic link …

Dynamic interaction graphs have been widely adopted to model the evolution of user-item
interactions over time. There are two crucial factors when modelling user preferences for link …

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