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
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Latest papers without code
Deep Node Ranking: an Algorithm for Structural Network Embedding and End-to-End Classification
•
Complex networks are used as an abstraction for systems modeling in physics, biology, sociology, and other areas.
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.
Hypergraph Convolution and Hypergraph Attention
•
To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i.e., hypergraph convolution and hypergraph attention.
Network Lens: Node Classification in Topologically Heterogeneous Networks
•
We study the problem of identifying different behaviors occurring in different parts of a large heterogenous network.
Graph Neural Networks with convolutional ARMA filters
•
Recent graph neural networks implement convolutional layers based on polynomial filters operating in the spectral domain.
Dynamic Graph Representation Learning via Self-Attention Networks
•
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization.
GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION
Graph Node-Feature Convolution for Representation Learning
•
Graph convolutional network (GCN) is an emerging neural network approach.
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
•
Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data.
Multi-layered Graph Embedding with Graph Convolutional Networks
•
In this paper, we study the problem of node embedding in multi-layered graphs and propose a deep method that embeds nodes using both relations (connections within and between layers of the graph) and nodes signals.
GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION
Pitfalls of Graph Neural Network Evaluation
•
We perform a thorough empirical evaluation of four prominent GNN models and show that considering different splits of the data leads to dramatically different rankings of models.