# Node Classification

778 papers with code • 122 benchmarks • 68 datasets

**Node Classification** is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.

**Node Classification** models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.

( Image credit: Fast Graph Representation Learning With PyTorch Geometric )

## Libraries

Use these libraries to find Node Classification models and implementations## Subtasks

## Most implemented papers

# Graph Attention Networks

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

# Semi-Supervised Classification with Graph Convolutional Networks

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.

# Modeling Relational Data with Graph Convolutional Networks

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

# Revisiting Semi-Supervised Learning with Graph Embeddings

We present a semi-supervised learning framework based on graph embeddings.

# Neural Message Passing for Quantum Chemistry

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.

# node2vec: Scalable Feature Learning for Networks

Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

# Inductive Representation Learning on Large Graphs

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.

# How Powerful are Graph Neural Networks?

Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.

# Benchmarking Graph Neural Networks

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

# DeepWalk: Online Learning of Social Representations

We present DeepWalk, a novel approach for learning latent representations of vertices in a network.