Bag of tricks for training deeper graph neural networks: A comprehensive benchmark study
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 …
plights in training deep architectures such as vanishing gradients and overfitting, it also …
Towards deep attention in graph neural networks: Problems and remedies
Graph neural networks (GNNs) learn the representation of graph-structured data, and their
expressiveness can be further enhanced by inferring node relations for propagation …
expressiveness can be further enhanced by inferring node relations for propagation …
Graph neural networks: Methods, applications, and opportunities
In the last decade or so, we have witnessed deep learning reinvigorating the machine
learning field. It has solved many problems in the domains of computer vision, speech …
learning field. It has solved many problems in the domains of computer vision, speech …
The snowflake hypothesis: Training deep GNN with one node one receptive field
Despite Graph Neural Networks demonstrating considerable promise in graph
representation learning tasks, GNNs predominantly face significant issues with over-fitting …
representation learning tasks, GNNs predominantly face significant issues with over-fitting …
Early-bird gcns: Graph-network co-optimization towards more efficient gcn training and inference via drawing early-bird lottery tickets
Abstract Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep
learning model for representation learning on graphs. However, it remains notoriously …
learning model for representation learning on graphs. However, it remains notoriously …
Tinygnn: Learning efficient graph neural networks
B Yan, C Wang, G Guo, Y Lou - Proceedings of the 26th ACM SIGKDD …, 2020 - dl.acm.org
Recently, Graph Neural Networks (GNNs) arouse a lot of research interest and achieve
great success in dealing with graph-based data. The basic idea of GNNs is to aggregate …
great success in dealing with graph-based data. The basic idea of GNNs is to aggregate …
Anti-symmetric dgn: a stable architecture for deep graph networks
Deep Graph Networks (DGNs) currently dominate the research landscape of learning from
graphs, due to their efficiency and ability to implement an adaptive message-passing …
graphs, due to their efficiency and ability to implement an adaptive message-passing …
L2-gcn: Layer-wise and learned efficient training of graph convolutional networks
Graph convolution networks (GCN) are increasingly popular in many applications, yet
remain notoriously hard to train over large graph datasets. They need to compute node …
remain notoriously hard to train over large graph datasets. They need to compute node …
Evaluating deep graph neural networks
Graph Neural Networks (GNNs) have already been widely applied in various graph mining
tasks. However, they suffer from the shallow architecture issue, which is the key impediment …
tasks. However, they suffer from the shallow architecture issue, which is the key impediment …
Ripple walk training: A subgraph-based training framework for large and deep graph neural network
Graph neural networks (GNNs) have achieved outstanding performance in learning graph-
structured data and various tasks. However, many current GNNs suffer from three common …
structured data and various tasks. However, many current GNNs suffer from three common …