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 101–110 of 927 papers

Title	Date	Tasks	Status	Hype
Graph Attention Networks	Oct 30, 2017	Document ClassificationGraph Attention	CodeCode Available	1
Graph Autoencoder for Graph Compression and Representation Learning	Mar 4, 2021	Data CompressionGraph Classification	CodeCode Available	1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks	Mar 26, 2020	Graph ClassificationGraph Learning	CodeCode Available	1
Graph Cross Networks with Vertex Infomax Pooling	Oct 5, 2020	ClassificationGeneral Classification	CodeCode Available	1
Graph Homomorphism Convolution	May 3, 2020	General ClassificationGraph Classification	CodeCode Available	1
Graph Inductive Biases in Transformers without Message Passing	May 27, 2023	Graph ClassificationGraph Regression	CodeCode Available	1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems	Aug 28, 2023	Collaborative FilteringGraph Classification	CodeCode Available	1
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing	Feb 17, 2021	Graph ClassificationGraph Learning	CodeCode Available	1
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications	Oct 17, 2021	Graph ClassificationInference Attack	CodeCode Available	1
Deep Graph Mapper: Seeing Graphs through the Neural Lens	Feb 10, 2020	Graph ClassificationGraph Representation Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 11 of 93Next →

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