92 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.

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

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

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

CVPR 2018 • rusty1s/pytorch_geometric •

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.

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.

SOTA for Graph Classification on IPC-lifted

DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION

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.

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

#2 best model for Node Classification on NELL

DOCUMENT CLASSIFICATION ENTITY EXTRACTION NODE CLASSIFICATION

dmlc/dgl •

•Accelerating research in the emerging field of deep graph learning requires new tools.

#8 best model for Node Classification on Cora

We present DeepWalk, a novel approach for learning latent representations of vertices in a network.

#3 best model for Node Classification on Wikipedia

ANOMALY DETECTION DOCUMENT CLASSIFICATION LANGUAGE MODELLING NODE CLASSIFICATION

NeurIPS 2017 • williamleif/GraphSAGE •

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.

#7 best model for Node Classification on PPI

ICLR 2018 • PetarV-/GAT •

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.

#2 best model for Skeleton Based Action Recognition on J-HMBD Early Action

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