Graph neural networks (GNNs) aim to learn well-trained representations in a lower-
dimension space for downstream tasks while preserving the topological structures. In recent …

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 …

Abstract The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks
(GNNs) is gradually attracting attention due to the low power consumption and high …

Traffic forecasting plays a vital role in the management of urban road networks and the
development of intelligent transportation systems. To effectively capture spatial and temporal …

Graph neural networks (GNNs) have enabled the automation of many web applications that
entail node classification on graphs, such as scam detection in social media and event …

Learning on evolving (dynamic) graphs has caught the attention of researchers as static
methods exhibit limited performance in this setting. The existing methods for dynamic graphs …

Node representation learning (NRL) generates numerical vectors (embeddings) for the
nodes of a graph. Structural NRL specifically assigns similar node embeddings for those …

Research and analysis of attacks on dynamic graph is beneficial for information systems to
investigate vulnerabilities and strength abilities in resisting malicious attacks. Existing …

Geo-sensory time series, such as the air quality and water distribution, are collected from
numerous sensors at different geospatial locations in the same time interval. Each sensor …

Continuous dynamic graph neural network (DGNN) methods have attracted increasing
attention owing to their ability to learn fine-grained temporal representations. Real-world …