170 papers with code ·
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.

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

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 Photo

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

Ranked #10 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 #2 on Graph Classification on REDDIT-B

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

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

Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

GRAPH CLASSIFICATION GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION

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

Ranked #17 on Node Classification on Cora

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

Ranked #2 on Node Classification on Wiki-Vote

DOCUMENT CLASSIFICATION GRAPH EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

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

DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.

Ranked #4 on Skeleton Based Action Recognition on SBU

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

Ranked #1 on Node Classification on USA Air-Traffic

DOCUMENT CLASSIFICATION ENTITY EXTRACTION USING GAN NODE CLASSIFICATION