324 papers with code • 71 benchmarks • 22 datasets

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.

( Image credit: Fast Graph Representation Learning With PyTorch Geometric )

Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].

Ranked #1 on Node Classification on Pubmed (F1 metric)

Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.

Ranked #2 on Graph Classification on CIFAR10 100k

In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC).

Ranked #1 on Node Classification on AMZ Comp

We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.

Ranked #15 on Node Classification on Citeseer

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.

Ranked #4 on Graph Classification on REDDIT-B

To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i. e., hypergraph convolution and hypergraph attention.

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.

Ranked #2 on Node Classification on Cora

Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.

Ranked #23 on Node Classification on Cora

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.

Ranked #1 on Graph Classification on IPC-grounded

We present a semi-supervised learning framework based on graph embeddings.

Ranked #1 on Node Classification on USA Air-Traffic