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
Graph Wavelet Neural Network
ICLR 2019
• benedekrozemberczki/GraphWaveletNeuralNetwork
• 
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform.
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.
Fisher-Bures Adversary Graph Convolutional Networks
We try to maximize the intrinsic scale of the permutation with a small budget while minimizing the loss based on the perturbed $G+\Delta{G}$.
Representation Learning for Heterogeneous Information Networks via Embedding Events
Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space.
Attributed Network Embedding via Subspace Discovery
In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space.
Enhanced Network Embedding with Text Information
24th International Conference on Pattern Recognition (ICPR) 2018 • benedekrozemberczki/TENE
TENE learns the representations of nodes under the guidance of both proximity matrix which captures the network structure and text cluster membership matrix derived from clustering for text information.
Attributed Network Embedding for Incomplete Structure Information
Network Embedding (NE) for such an attributed network by considering both structure and attribute information has recently attracted considerable attention, since each node embedding is simply a unified low-dimension vector representation that makes downstream tasks e. g. link prediction more efficient and much easier to realize.
Node Embedding with Adaptive Similarities for Scalable Learning over Graphs
Moreover, an algorithmic scheme is proposed for training the model parameters effieciently and in an unsupervised manner.
DynamicGEM: A Library for Dynamic Graph Embedding Methods
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.
Role action embeddings: scalable representation of network positions
• georgeberry/role-action-embeddings
• 
It combines a within-node loss function and a graph neural network (GNN) architecture to place nodes with similar local neighborhoods close in embedding space.