Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

Graph Neural Networks (GNNs) have evolved to understand graph structures through
recursive exchanges and aggregations among nodes. To enhance robustness, self …

Many real-world graph learning tasks require handling dynamic graphs where new nodes
and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic …

Graph Neural Networks (GNNs) have shown superior performance in modeling graph data.
Existing studies have shown that a lot of GNNs perform well on homophilic graphs while …

With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG)
have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown …

The propagation of rumours on social media poses an important threat to societies, so that
various techniques for graph-based rumour detection have been proposed recently. Existing …

Dynamic graphs are ubiquitous across disciplines where observations usually change over
time. Regressions on dynamic graphs often contribute to diverse critical tasks, such as …

Graph Neural Networks (GNNs) have shown expressive performance on graph
representation learning by aggregating information from neighbors. Recently, some studies …

Graph neural networks (GNNs) have shown ad-vantages in graph-based analysis tasks.
However, most existing methods have the homogeneity assumption and show poor …