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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Latest papers with code
DeepGCNs: Making GCNs Go as Deep as CNNs
We believe that the insights in this work will open a lot of avenues for future research on GCNs and transfer to further tasks not explored in this work.
SOTA for Node Classification on PPI
NODE CLASSIFICATION OBJECT CLASSIFICATION SEMANTIC SEGMENTATION
Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs
From social networks to protein complexes to disease genomes to visual data, hypergraphs are everywhere.
GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning
We present GraphMix, a regularization technique for Graph Neural Network based semi-supervised object classification, leveraging the recent advances in the regularization of classical deep neural networks.
GRAPH NEURAL NETWORK NODE CLASSIFICATION OBJECT CLASSIFICATION
GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation
• anonymous-sourcecode/GResNet
Analysis about the causes of the suspended animation problem with existing GNNs will be provided in this paper, whereas several other peripheral factors that will impact the problem will be reported as well.
#2 best model for
Node Classification
on Cora
(using extra training data)
Kernel Node Embeddings
Learning representations of nodes in a low dimensional space is a crucial task with many interesting applications in network analysis, including link prediction and node classification.
Graph Representation Ensemble Learning
• dihuang0220/GraphEnsembleLearning
In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently.
Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs
• dmlc/dgl
• 
Accelerating research in the emerging field of deep graph learning requires new tools.
#10 best model for
Node Classification
on Cora
(using extra training data)
Adversarial Training Methods for Network Embedding
Most of existing works aim to preserve different network structures and properties in low-dimensional embedding vectors, while neglecting the existence of noisy information in many real-world networks and the overfitting issue in the embedding learning process.
GEMSEC: Graph Embedding with Self Clustering
ASONAM 2019
• benedekrozemberczki/GEMSEC
• 
In this paper we propose GEMSEC - a graph embedding algorithm which learns a clustering of the nodes simultaneously with the embedding.
COMMUNITY DETECTION GRAPH EMBEDDING NETWORK EMBEDDING NODE CLASSIFICATION
EdMot: An Edge Enhancement Approach for Motif-aware Community Detection
KDD 2019 • benedekrozemberczki/EdMot
Based on the new edge set, the original connectivity structure of the input network is enhanced to generate a rewired network, whereby the motif-based higher-order structure is leveraged and the hypergraph fragmentation issue is well addressed.
SOTA for Community Detection on Cora