Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …

Graph Neural Networks (GNNs) have recently become increasingly popular due to their
ability to learn complex systems of relations or interactions arising in a broad spectrum of …

Temporal graph representation learning has drawn significant attention for the prevalence of
temporal graphs in the real world. However, most existing works resort to taking discrete …

Graph neural networks (GNNs) have been broadly studied on dynamic graphs for their
representation learning, majority of which focus on graphs with homogeneous structures in …

Recent years have seen a surge in research on dynamic graph representation learning,
which aims to model temporal graphs that are dynamic and evolving constantly over time …

Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph
representation learning and have demonstrated superior performance in many real-world …

Recent progress in research on deep graph networks (DGNs) has led to a maturation of the
domain of learning on graphs. Despite the growth of this research field, there are still …

Neural forecasting of spatiotemporal time series drives both research and industrial
innovation in several relevant application domains. Graph neural networks (GNNs) are often …

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 …

Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …