# Graph Classification

262 papers with code • 62 benchmarks • 34 datasets

( Image credit: Hierarchical Graph Pooling with Structure Learning )

## Libraries

Use these libraries to find Graph Classification models and implementations## 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.

# How Powerful are Graph Neural Networks?

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

# 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.

# Hierarchical Graph Representation Learning with Differentiable Pooling

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.

# 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.

# ImageNet Classification with Deep Convolutional Neural Networks

We trained a large, deep convolutional neural network to classify the 1. 3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes.

# LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.

# GNNExplainer: Generating Explanations for Graph Neural Networks

We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.