About

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 )

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Subtasks

Datasets

Greatest papers with code

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

KDD 2019 google-research/google-research

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)

4 GRAPH CLUSTERING LINK PREDICTION NODE CLASSIFICATION

Principal Neighbourhood Aggregation for Graph Nets

NeurIPS 2020 rusty1s/pytorch_geometric

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

GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION

Diffusion Improves Graph Learning

NeurIPS 2019 rusty1s/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).

GRAPH LEARNING NODE CLASSIFICATION

Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks

9 Apr 2019rusty1s/pytorch_geometric

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

NODE CLASSIFICATION REPRESENTATION LEARNING

Fast Graph Representation Learning with PyTorch Geometric

6 Mar 2019rusty1s/pytorch_geometric

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

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

Hypergraph Convolution and Hypergraph Attention

23 Jan 2019rusty1s/pytorch_geometric

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.

NODE CLASSIFICATION REPRESENTATION LEARNING

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

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.

GRAPH CLASSIFICATION NODE CLASSIFICATION SUPERPIXEL IMAGE CLASSIFICATION

Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

15 Sep 2020dmlc/dgl

Motivated by this observation, we propose a graph representation learning method called Graph InfoClust (GIC), that seeks to additionally capture cluster-level information content.

GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION NODE CLUSTERING

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

3 Sep 2019dmlc/dgl

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

GRAPH LEARNING NODE CLASSIFICATION

Semi-Supervised Classification with Graph Convolutional Networks

9 Sep 2016tkipf/gcn

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.

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