To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

Graph Neural Networks (GNNs) have received significant attention for demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

Large-scale graph training is a notoriously challenging problem for graph neural networks
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …

Recent studies have shown that graph neural networks (GNNs) exhibit strong biases
towards the node degree: they usually perform satisfactorily on high-degree nodes with rich …

Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …

While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-
Euclidean structural data, they are difficult to be deployed in real applications due to the …

Training deep graph neural networks (GNNs) is notoriously hard. Besides the standard
plights in training deep architectures such as vanishing gradients and overfitting, it also …

Graph collaborative filtering (GCF) is a popular technique for capturing high-order
collaborative signals in recommendation systems. However, GCF's bipartite adjacency …

Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item
interactions are entirely unavailable. The well-established, dominating identity (ID)-based …

Finding the proper depth $ d $ of a graph convolutional network (GCN) that provides strong
representation ability has drawn significant attention, yet nonetheless largely remains an …