[HTML][HTML] Graph neural networks: A review of methods and applications
Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …
information among elements. Modeling physics systems, learning molecular fingerprints …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Data augmentation for deep graph learning: A survey
Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …
demonstrated remarkable performance on numerous graph learning tasks. To address the …
Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Many widely used datasets for graph machine learning tasks have generally been
homophilous, where nodes with similar labels connect to each other. Recently, new Graph …
homophilous, where nodes with similar labels connect to each other. Recently, new Graph …
Training graph neural networks with 1000 layers
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on
increasingly large graph datasets with millions of nodes and edges. However, memory …
increasingly large graph datasets with millions of nodes and edges. However, memory …
Decoupling the depth and scope of graph neural networks
State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the
graph and model sizes. On large graphs, increasing the model depth often means …
graph and model sizes. On large graphs, increasing the model depth often means …
Adaptive universal generalized pagerank graph neural network
In many important graph data processing applications the acquired information includes
both node features and observations of the graph topology. Graph neural networks (GNNs) …
both node features and observations of the graph topology. Graph neural networks (GNNs) …
A comprehensive survey on deep graph representation learning methods
There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …
representation learning aims to produce graph representation vectors to represent the …
Combining label propagation and simple models out-performs graph neural networks
Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
However, there is relatively little understanding of why GNNs are successful in practice and …
However, there is relatively little understanding of why GNNs are successful in practice and …
Multi-scale attributed node embedding
We present network embedding algorithms that capture information about a node from the
local distribution over node attributes around it, as observed over random walks following an …
local distribution over node attributes around it, as observed over random walks following an …