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 …

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 …

Recent advances in data processing have stimulated the demand for learning graphs of very
large scales. Graph neural networks (GNNs), being an emerging and powerful approach in …

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural
Networks (GNNs). AGS-GNN exploits the node features and the connectivity structure of a …

Graph convolutional networks have significant advantages in dealing with graph-structured
data, but most existing methods usually potentially assume that nodes belonging to the …

Graph representation learning is an emerging task for effectively embedding graph-
structured data with learned features. Among them, Subgraph-based GRL (SGRL) methods …

We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge
graph (KG) reasoning. For the first time, we link the path redundancy issue in the state-of-the …

Graph Neural Networks (GNNs) have aroused increasing research attention for their
effectiveness on graph mining tasks. However, full-batch training methods based on …

With the recent advancements in graph neural networks (GNNs), spectral GNNs have
received increasing popularity by virtue of their specialty in capturing graph signals in the …

Graph Neural Networks (GNNs) have shown promising performance in various graph
learning tasks, but at the cost of resource-intensive computations. The primary overhead of …