Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

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 …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

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 …

Semi-supervised learning on graphs is an important problem in the machine learning area.
In recent years, state-of-the-art classification methods based on graph neural networks …

Graph neural networks (GNNs) have achieved great success in many graph-based
applications. However, the enormous size and high sparsity level of graphs hinder their …

Graph (structure) augmentation aims to perturb the graph structure through heuristic or
probabilistic rules, enabling the nodes to capture richer contextual information and thus …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

To bridge the gaps between topology-aware Graph Neural Networks (GNNs) and inference-
efficient Multi-Layer Perceptron (MLPs), GLNN proposes to distill knowledge from a well …

Recent years have witnessed the great success of Graph Neural Networks (GNNs) in
handling graph-related tasks. However, MLPs remain the primary workhorse for practical …