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 751–760 of 927 papers

Title	Date	Tasks	Status	Hype
A Hierarchy of Graph Neural Networks Based on Learnable Local Features	Nov 13, 2019	General ClassificationGraph Classification	—Unverified	0
Composition-based Multi-Relational Graph Convolutional Networks	Nov 8, 2019	General ClassificationGraph Classification	CodeCode Available	1
Fundamental Limits of Deep Graph Convolutional Networks	Oct 28, 2019	Graph ClassificationGraph Representation Learning	—Unverified	0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning	Oct 26, 2019	Bayesian InferenceGeneral Classification	—Unverified	0
Understanding Isomorphism Bias in Graph Data Sets	Oct 26, 2019	General ClassificationGraph Classification	CodeCode Available	0
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling	Oct 24, 2019	Graph ClassificationRepresentation Learning	CodeCode Available	0
Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction	Oct 24, 2019	General ClassificationGraph Classification	—Unverified	0
Temporal Graph Kernels for Classifying Dissemination Processes	Oct 14, 2019	General ClassificationGraph Classification	—Unverified	0
Rethinking Kernel Methods for Node Representation Learning on Graphs	Oct 6, 2019	ClassificationGeneral Classification	CodeCode Available	0
Evolution of Graph Classifiers	Oct 4, 2019	Graph Classification	CodeCode Available	0

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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