98 papers with code ยท
Graphs

The node classification task is one where the algorithm has to determine the labelling of samples (represented as nodes) by looking at the labels of their neighbours.

Interest has been rising lately towards methods representing data in non-Euclidean spaces, e. g. hyperbolic or spherical.

Graph convolutional neural networks have demonstrated promising performance in attributed graph learning, thanks to the use of graph convolution that effectively combines graph structures and node features for learning node representations.

Moreover, we also studied how to learn a universal policy for labeling nodes on graphs with multiple training graphs and then transfer the learned policy to unseen graphs.

Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification.

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms.

Over-fitting and over-smoothing are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification.

Despite the impressive success of graph convolutional networks (GCNs) on numerous applications, training on large-scale sparse networks remains challenging.

Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.

Graph neural networks (GNN) such as GCN, GAT, MoNet have achieved state-of-the-art results on semi-supervised learning on graphs.

Towards the challenging problem of semi-supervised node classification, there have been extensive studies.