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Graph Classification

382 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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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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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.

17
Paper
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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

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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