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 …

Driven by the powerful representation ability of Graph Neural Networks (GNNs), plentiful
GNN models have been widely deployed in many real-world applications. Nevertheless …

Spatio-temporal (ST) prediction has garnered a De facto attention in earth sciences, such as
meteorological prediction, human mobility perception. However, the scarcity of data coupled …

Graphs are structured data that models complex relations between real-world entities.
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have raised growing concerns. As one of the most …

Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural
Networks (GNNs) in modeling long-range interactions. Despite their success, existing …

Heterogeneous graph neural networks (HGNNs) have significantly propelled the information
retrieval (IR) field. Still, the effectiveness of HGNNs heavily relies on high-quality labels …

Graph Neural Networks (GNNs) are increasingly being used for a variety of ML applications
on graph data. As graph data does not follow the independently and identically distributed …

Graph Neural Networks (GNNs) are increasingly being used for a variety of ML applications
on graph data. As graph data does not follow the independently and identically distributed …