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 126–150 of 927 papers

Title	Date	Tasks	Status	Hype
Nested Graph Neural Networks	Oct 25, 2021	Graph ClassificationGraph Neural Network	CodeCode Available	1
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications	Oct 17, 2021	Graph ClassificationInference Attack	CodeCode Available	1
Well-classified Examples are Underestimated in Classification with Deep Neural Networks	Oct 13, 2021	ClassificationGraph Classification	CodeCode Available	1
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification	Oct 12, 2021	ClassificationComputational Efficiency	CodeCode Available	1
Inference Attacks Against Graph Neural Networks	Oct 6, 2021	Graph ClassificationGraph Embedding	CodeCode Available	1
Orthogonal Graph Neural Networks	Sep 23, 2021	AttributeGraph Classification	CodeCode Available	1
A Meta-Learning Approach for Training Explainable Graph Neural Networks	Sep 20, 2021	Graph ClassificationMeta-Learning	CodeCode Available	1
An Empirical Study of Graph Contrastive Learning	Sep 2, 2021	Graph ClassificationManagement	CodeCode Available	1
Pooling Architecture Search for Graph Classification	Aug 24, 2021	ClassificationGraph Classification	CodeCode Available	1
Global Self-Attention as a Replacement for Graph Convolution	Aug 7, 2021	Edge ClassificationGraph Classification	CodeCode Available	1
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data	Jul 5, 2021	AttributeDrug Discovery	CodeCode Available	1
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs	Jul 3, 2021	ClassificationGraph Classification	CodeCode Available	1
Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks	Jul 3, 2021	Combinatorial OptimizationGraph Classification	CodeCode Available	1
Edge Representation Learning with Hypergraphs	Jun 30, 2021	Graph ClassificationGraph Reconstruction	CodeCode Available	1
Federated Graph Classification over Non-IID Graphs	Jun 25, 2021	ClassificationClustering	CodeCode Available	1
Weisfeiler and Lehman Go Cellular: CW Networks	Jun 23, 2021	Graph ClassificationGraph Property Prediction	CodeCode Available	1
Evaluating Modules in Graph Contrastive Learning	Jun 15, 2021	Contrastive LearningGraph Classification	CodeCode Available	1
Convergent Graph Solvers	Jun 3, 2021	Graph ClassificationGraph Neural Network	CodeCode Available	1
Mixup for Node and Graph Classification	Jun 1, 2021	ClassificationData Augmentation	CodeCode Available	1
How Attentive are Graph Attention Networks?	May 30, 2021	Graph AttentionGraph Classification	CodeCode Available	1
User Preference-aware Fake News Detection	Apr 25, 2021	Fact CheckingFake News Detection	CodeCode Available	1
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning	Apr 20, 2021	ClusteringGeneral Classification	CodeCode Available	1
Parameterized Hypercomplex Graph Neural Networks for Graph Classification	Mar 30, 2021	ClassificationGeneral Classification	CodeCode Available	1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification	Mar 22, 2021	Continual LearningGeneral Classification	CodeCode Available	1
Size-Invariant Graph Representations for Graph Classification Extrapolations	Mar 8, 2021	ClassificationGeneral 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