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 411–420 of 927 papers

Title	Date	Tasks	Status
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution	May 12, 2021	Entity ResolutionGraph Classification	—Unverified
Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview	Aug 1, 2019	Graph ClassificationGraph Neural Network	—Unverified
GPS: A Policy-driven Sampling Approach for Graph Representation Learning	Dec 29, 2021	Graph ClassificationGraph Representation Learning	—Unverified
Globally Interpretable Graph Learning via Distribution Matching	Jun 18, 2023	Graph ClassificationGraph Learning	—Unverified
Adversarial Attack on Hierarchical Graph Pooling Neural Networks	May 23, 2020	Adversarial AttackClassification	—Unverified
Going beyond persistent homology using persistent homology	Nov 10, 2023	Graph Classification	—Unverified
Distribution Preserving Graph Representation Learning	Feb 27, 2022	Graph ClassificationGraph Learning	—Unverified
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification	Feb 5, 2022	ClassificationGraph Classification	—Unverified
Diversified Multiscale Graph Learning with Graph Self-Correction	Mar 17, 2021	DiversityEnsemble Learning	—Unverified
HaarPooling: Graph Pooling with Compressive Haar Basis	Sep 25, 2019	Graph Classification	—Unverified

Show:10 25 50

← PrevPage 42 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	DUGNN	Accuracy	85.5	—	Unverified
9	δ-2-LWL	Accuracy	85.5	—	Unverified
10	CIN++	Accuracy	85.3	—	Unverified