Graph Classification

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 )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 201–225 of 927 papers

Title	Date	Tasks	Status	Hype
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures	Feb 27, 2020	Active LearningClassification	CodeCode Available	1
Memory-Based Graph Networks	Feb 21, 2020	Graph Classificationregression	CodeCode Available	1
Deep Graph Mapper: Seeing Graphs through the Neural Lens	Feb 10, 2020	Graph ClassificationGraph Representation Learning	CodeCode Available	1
Graph Neural Distance Metric Learning with Graph-Bert	Feb 9, 2020	ClusteringGraph Classification	CodeCode Available	1
Segmented Graph-Bert for Graph Instance Modeling	Feb 9, 2020	Graph ClassificationRepresentation Learning	CodeCode Available	1
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation	Feb 6, 2020	Collaborative FilteringGraph Classification	CodeCode Available	1
Structure-Feature based Graph Self-adaptive Pooling	Jan 30, 2020	Graph Classification	CodeCode Available	1
Adversarial Attack on Community Detection by Hiding Individuals	Jan 22, 2020	Adversarial AttackCommunity Detection	CodeCode Available	1
Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis	Jan 10, 2020	Functional ConnectivityGeneral Classification	CodeCode Available	1
A Fair Comparison of Graph Neural Networks for Graph Classification	Dec 20, 2019	General ClassificationGraph Classification	CodeCode Available	1
Hierarchical Graph Pooling with Structure Learning	Nov 14, 2019	Graph ClassificationGraph Neural Network	CodeCode Available	1
Composition-based Multi-Relational Graph Convolutional Networks	Nov 8, 2019	General ClassificationGraph Classification	CodeCode Available	1
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization	Jul 31, 2019	Graph ClassificationMolecular Property Prediction	CodeCode Available	1
Spectral Clustering with Graph Neural Networks for Graph Pooling	Jun 30, 2019	ClusteringGraph Classification	CodeCode Available	1
GOT: An Optimal Transport framework for Graph comparison	Jun 5, 2019	Graph Classification	CodeCode Available	1
Strategies for Pre-training Graph Neural Networks	May 29, 2019	Graph ClassificationMolecular Property Prediction	CodeCode Available	1
GNNExplainer: Generating Explanations for Graph Neural Networks	Mar 10, 2019	BIG-bench Machine LearningExplainable artificial intelligence	CodeCode Available	1
Fast Graph Representation Learning with PyTorch Geometric	Mar 6, 2019	GPUGraph Classification	CodeCode Available	1
Fake News Detection on Social Media using Geometric Deep Learning	Feb 10, 2019	Common Sense ReasoningDeep Learning	CodeCode Available	1
Variational Recurrent Neural Networks for Graph Classification	Feb 7, 2019	ClassificationGeneral Classification	CodeCode Available	1
Streaming Graph Neural Networks	Oct 24, 2018	Community DetectionGeneral Classification	CodeCode Available	1
How Powerful are Graph Neural Networks?	Oct 1, 2018	General ClassificationGraph Classification	CodeCode Available	1
Hierarchical Graph Representation Learning with Differentiable Pooling	Jun 22, 2018	General ClassificationGraph Classification	CodeCode Available	1
Anonymous Walk Embeddings	May 30, 2018	General ClassificationGraph Classification	CodeCode Available	1
Graph Attention Networks	Oct 30, 2017	Document ClassificationGraph Attention	CodeCode Available	1

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All datasets PROTEINS MUTAG NCI1 D&D ENZYMES IMDb-B COLLAB PTC IMDb-M NCI109 CIFAR10 100k REDDIT-B

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	GIN-0	Accuracy	762	—	Unverified
2	HGP-SL	Accuracy	84.91	—	Unverified
3	rLap (unsupervised)	Accuracy	84.3	—	Unverified
4	TFGW ADJ (L=2)	Accuracy	82.9	—	Unverified
5	FIT-GNN	Accuracy	82.1	—	Unverified
6	DUGNN	Accuracy	81.7	—	Unverified
7	MEWISPool	Accuracy	80.71	—	Unverified
8	CIN++	Accuracy	80.5	—	Unverified
9	MAGPool	Accuracy	80.36	—	Unverified
10	SAEPool	Accuracy	80.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Evolution of Graph Classifiers	Accuracy	100	—	Unverified
2	MEWISPool	Accuracy	96.66	—	Unverified
3	TFGW ADJ (L=2)	Accuracy	96.4	—	Unverified
4	GIUNet	Accuracy	95.7	—	Unverified
5	G_Inception	Accuracy	95	—	Unverified
6	GIC	Accuracy	94.44	—	Unverified
7	CIN++	Accuracy	94.4	—	Unverified
8	sGIN	Accuracy	94.14	—	Unverified
9	CAN	Accuracy	94.1	—	Unverified
10	Deep WL SGN(0,1,2)	Accuracy	93.68	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TFGW ADJ (L=2)	Accuracy	88.1	—	Unverified
2	WKPI-kmeans	Accuracy	87.2	—	Unverified
3	FGW wl h=4 sp	Accuracy	86.42	—	Unverified
4	WL-OA Kernel	Accuracy	86.1	—	Unverified
5	WL-OA	Accuracy	86.1	—	Unverified
6	FGW wl h=2 sp	Accuracy	85.82	—	Unverified
7	WWL	Accuracy	85.75	—	Unverified
8	δ-2-LWL	Accuracy	85.5	—	Unverified
9	DUGNN	Accuracy	85.5	—	Unverified
10	CIN++	Accuracy	85.3	—	Unverified