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Graph Classification

380 papers with code • 65 benchmarks • 46 datasets

Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.

( Image credit: Hierarchical Graph Pooling with Structure Learning )

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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.

90
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Semi-Supervised Classification with Graph Convolutional Networks

tkipf/pygcn • • 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.

51
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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.

27
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Code

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.

19
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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.

18
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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.

18
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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.

16
Paper
Code

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.

16
Paper
Code

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.

14
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Gated Graph Sequence Neural Networks

dmlc/dgl • • 17 Nov 2015

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.

13
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Code
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