We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
#2 best model for Graph Classification on REDDIT-B
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.
SOTA for Node Classification on Wikipedia
With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered.
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.
Capturing such evolution is key to predicting the properties of unseen networks.
We examine two fundamental tasks associated with graph representation learning: link prediction and node classification.
#2 best model for Link Prediction on Citeseer (Accuracy metric)
We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification.
#18 best model for Node Classification on Citeseer
In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus.
However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural network layers.