Node Classification
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.
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Greatest papers with code
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks
We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.
Fast Graph Representation Learning with 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.
SOTA for Graph Classification on COLLAB
GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING
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.
Semi-Supervised Classification with Graph Convolutional Networks
• tkipf/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.
SOTA for Node Classification on NELL
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
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.
Revisiting Semi-Supervised Learning with Graph Embeddings
• tkipf/gcn
•
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
DeepWalk: Online Learning of Social Representations
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
Inductive Representation Learning on Large Graphs
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.
#10 best model for
Node Classification
on PubMed with Public Split: fixed 20 nodes per class
Graph Attention Networks
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.
#3 best model for
Document Classification
on Cora
DOCUMENT CLASSIFICATION GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION
LINE: Large-scale Information Network Embedding
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.
#5 best model for
Node Classification
on Wikipedia
GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION