Graph Classification

262 papers with code • 62 benchmarks • 34 datasets

Libraries

Use these libraries to find Graph Classification models and implementations

Most implemented papers

Graph Attention Networks

PetarV-/GAT ICLR 2018

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

dmlc/dgl 9 Sep 2016

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

tkipf/relational-gcn 17 Mar 2017

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

How Powerful are Graph Neural Networks?

weihua916/powerful-gnns ICLR 2019

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

Inductive Representation Learning on Large Graphs

williamleif/GraphSAGE NeurIPS 2017

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

dmlc/dgl NeurIPS 2018

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

graphdeeplearning/benchmarking-gnns 2 Mar 2020

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

worksheets/0xfafccca5 NeurIPS 2012

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

gusye1234/pytorch-light-gcn 6 Feb 2020

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

RexYing/gnn-model-explainer NeurIPS 2019

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