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
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
GraphSAINT: Graph Sampling Based Inductive Learning Method
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training.
#4 best model for
Node Classification
on PPI
GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION
Signed Graph Attention Networks
Recently, Graph Neural Network (GNN) is proposed as a general and powerful framework to handle tasks on graph data, e. g., node embedding, link prediction and node classification.
GRAPH NEURAL NETWORK LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION
Graph Star Net for Generalized Multi-Task Learning
• graph-star-team/graph_star
• 
In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, graph classification and link prediction.
SOTA for Link Prediction on Cora
GRAPH CLASSIFICATION LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION SENTIMENT ANALYSIS TEXT CLASSIFICATION
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
Motivation: Graph embedding learning which aims to automatically learn low-dimensional node representations has drawn increasing attention in recent years.
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems.
Triple2Vec: Learning Triple Embeddings from Knowledge Graphs
• Chrisackerman1/Triple2Vec-Learning-Triple-Embeddings-from-Knowledge-Graphs
We show that directly applying existing embedding techniques on the nodes of the line graph to learn edge embeddings is not enough in the context of knowledge graphs.
Graph Attention Auto-Encoders
Moreover, node representations are regularized to reconstruct the graph structure.
Meta-GNN: On Few-shot Node Classification in Graph Meta-learning
Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i. e., acquiring new knowledge and skills with little or even no demonstration.