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 176–200 of 927 papers

Title	Date	Tasks	Status	Hype
Learning Graph Normalization for Graph Neural Networks	Sep 24, 2020	Graph ClassificationGraph Regression	CodeCode Available	1
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training	Sep 7, 2020	Graph ClassificationGraph Neural Network	CodeCode Available	1
Lifelong Graph Learning	Sep 1, 2020	Action RecognitionContinual Learning	CodeCode Available	1
Quaternion Graph Neural Networks	Aug 12, 2020	General ClassificationGraph Classification	CodeCode Available	1
Detecting Beneficial Feature Interactions for Recommender Systems	Aug 2, 2020	Graph ClassificationGraph Neural Network	CodeCode Available	1
Second-Order Pooling for Graph Neural Networks	Jul 20, 2020	Graph ClassificationGraph Representation Learning	CodeCode Available	1
TUDataset: A collection of benchmark datasets for learning with graphs	Jul 16, 2020	Graph ClassificationGraph Neural Network	CodeCode Available	1
Multilevel Graph Matching Networks for Deep Graph Similarity Learning	Jul 8, 2020	Graph ClassificationGraph Matching	CodeCode Available	1
Simple and Deep Graph Convolutional Networks	Jul 4, 2020	Graph ClassificationGraph Regression	CodeCode Available	1
Path Integral Based Convolution and Pooling for Graph Neural Networks	Jun 29, 2020	Graph ClassificationGraph Regression	CodeCode Available	1
Graph Backdoor	Jun 21, 2020	Backdoor AttackDescriptive	CodeCode Available	1
Backdoor Attacks to Graph Neural Networks	Jun 19, 2020	Backdoor AttackGeneral Classification	CodeCode Available	1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training	Jun 17, 2020	Contrastive LearningGeneral Classification	CodeCode Available	1
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting	Jun 16, 2020	Graph ClassificationGraph Neural Network	CodeCode Available	1
Wasserstein Embedding for Graph Learning	Jun 16, 2020	Computational EfficiencyGraph Classification	CodeCode Available	1
Contrastive Multi-View Representation Learning on Graphs	Jun 10, 2020	General ClassificationGraph Classification	CodeCode Available	1
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models	May 16, 2020	Graph ClassificationNode Classification	CodeCode Available	1
Graph Homomorphism Convolution	May 3, 2020	General ClassificationGraph Classification	CodeCode Available	1
Principal Neighbourhood Aggregation for Graph Nets	Apr 12, 2020	Graph ClassificationGraph Regression	CodeCode Available	1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks	Mar 26, 2020	Graph ClassificationGraph Learning	CodeCode Available	1
Universal Function Approximation on Graphs	Mar 14, 2020	Dependency ParsingGraph Classification	CodeCode Available	1
Learning distributed representations of graphs with Geo2DR	Mar 12, 2020	GPUGraph Classification	CodeCode Available	1
Convolutional Kernel Networks for Graph-Structured Data	Mar 11, 2020	Graph Classification	CodeCode Available	1
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs	Mar 10, 2020	BIG-bench Machine LearningClustering	CodeCode Available	1
COPT: Coordinated Optimal Transport for Graph Sketching	Mar 9, 2020	Graph Classification	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	DUGNN	Accuracy	85.5	—	Unverified
9	δ-2-LWL	Accuracy	85.5	—	Unverified
10	CIN++	Accuracy	85.3	—	Unverified