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.
State-of-the-art leaderboards
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Greatest papers with code
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. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions.
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. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.
#5 best model for
Node Classification on Cora
Revisiting Semi-Supervised Learning with Graph Embeddings
• tkipf/gcn
•
We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph.
#2 best model for
Node Classification on NELL
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. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions.
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. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes.
DeepWalk: Online Learning of Social Representations
We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models.
#3 best model for
Node Classification on Wikipedia
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. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes.
#5 best model for
Node Classification on Wikipedia
GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
• SeongokRyu/Graph-neural-networks
The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs.
COMMUNITY DETECTION IMAGE CLASSIFICATION MATRIX COMPLETION NODE CLASSIFICATION
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices.
SOTA for Node Classification on BlogCatalog
GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION
struc2vec: Learning Node Representations from Structural Identity
Structural identity is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. Numerical experiments indicate that state-of-the-art techniques for learning node representations fail in capturing stronger notions of structural identity, while struc2vec exhibits much superior performance in this task, as it overcomes limitations of prior approaches.
#2 best model for
Node Classification on BlogCatalog