A graph structure is a powerful mathematical abstraction, which can not only represent
information about individuals but also capture the interactions between individuals for …

Graph representation learning has re-emerged as a fascinating research topic due to the
successful application of graph convolutional networks (GCNs) for graphs and inspires …

Continual learning on graph data has recently attracted paramount attention for its aim to
resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially …

Graph neural networks (GNNs) are powerful models to handle graph data and can achieve
state-of-the-art in many critical tasks including node classification and link prediction …

Learning unbiased node representations for imbalanced samples in the graph has become
a more remarkable and important topic. For the graph, a significant challenge is that the …

Link prediction is an important learning task for graph-structured data. In this paper, we
propose a novel topological approach to characterize interactions between two nodes. Our …

Memory replay, which stores a subset of historical data from previous tasks to replay while
learning new tasks, exhibits state-of-the-art performance for various continual learning …

Graph neural networks (GNNs) have achieved great success in many graph-based tasks.
Much work is dedicated to empowering GNNs with adaptive locality ability, which enables …

Graph clustering is a longstanding research topic, and has achieved remarkable success
with the deep learning methods in recent years. Nevertheless, we observe that several …

Information diffusion prediction is fundamental to understand the structure and organization
of the online social networks, and plays a crucial role to blocking rumor spread, influence …