Graph Classification

196 papers with code • 54 benchmarks • 31 datasets

Greatest papers with code

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

google-research/google-research 21 Apr 2019

Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph.

Feature Engineering Graph Attention +2

Principal Neighbourhood Aggregation for Graph Nets

rusty1s/pytorch_geometric NeurIPS 2020

Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.

Graph Classification Graph Regression +1

Fast Graph Representation Learning with PyTorch Geometric

rusty1s/pytorch_geometric 6 Mar 2019

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.

Graph Classification Graph Representation Learning +2

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

rusty1s/pytorch_geometric CVPR 2018

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.

General Classification Graph Classification +2

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

microsoft/recommenders 6 Feb 2020

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

Collaborative Filtering Graph Classification +1

Semi-Supervised Classification with Graph Convolutional Networks

tkipf/gcn 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.

Classification Document Classification +5

Graph Attention Networks

labmlai/annotated_deep_learning_paper_implementations 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.

Document Classification Graph Attention +7

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.

Graph Classification Graph Regression +3

ImageNet Classification with Deep Convolutional Neural Networks

PaddlePaddle/PaddleClas 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.

Classification General Classification +2

Structural Deep Network Embedding

shenweichen/GraphEmbedding KDD 2016

Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem.

Graph Classification Link Prediction +1