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 651–675 of 927 papers

Title	Date	Tasks	Status
Revisiting 2D Convolutional Neural Networks for Graph-based Applications	May 23, 2021	Computational EfficiencyGraph Classification	—Unverified
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms	May 14, 2021	Graph ClassificationGraph Learning	—Unverified
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution	May 12, 2021	Entity ResolutionGraph Classification	—Unverified
Structure-Aware Hierarchical Graph Pooling using Information Bottleneck	Apr 27, 2021	General ClassificationGraph Classification	CodeCode Available
Identity Inference on Blockchain using Graph Neural Network	Apr 14, 2021	Graph ClassificationGraph Mining	CodeCode Available
Quadratic GCN for Graph Classification	Apr 14, 2021	ClassificationGeneral Classification	CodeCode Available
A Hyperbolic-to-Hyperbolic Graph Convolutional Network	Apr 14, 2021	General ClassificationGraph Classification	—Unverified
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning	Apr 13, 2021	Graph ClassificationGraph Matching	—Unverified
Smart Vectorizations for Single and Multiparameter Persistence	Apr 10, 2021	Anomaly DetectionGraph Classification	CodeCode Available
Scaling up graph homomorphism for classification via sampling	Apr 8, 2021	BIG-bench Machine LearningClassification	—Unverified
GABO: Graph Augmentations with Bi-level Optimization	Apr 1, 2021	Bilevel OptimizationData Augmentation	—Unverified
Unified Graph Structured Models for Video Understanding	Mar 29, 2021	Action DetectionGraph Classification	—Unverified
Graph Classification by Mixture of Diverse Experts	Mar 29, 2021	ClassificationGeneral Classification	—Unverified
GraphDIVE: Graph Classification by Mixture of Diverse Experts	Mar 21, 2021	ClassificationGraph Classification	CodeCode Available
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models	Mar 19, 2021	DecoderGraph Classification	—Unverified
Diversified Multiscale Graph Learning with Graph Self-Correction	Mar 17, 2021	DiversityEnsemble Learning	—Unverified
Should Graph Neural Networks Use Features, Edges, Or Both?	Mar 11, 2021	ClassificationGeneral Classification	—Unverified
Scaling Up Graph Homomorphism Features with Efficient Data Structures	Mar 8, 2021	Graph Classification	—Unverified
Sanity Check for Persistence Diagrams	Mar 8, 2021	Graph ClassificationTopological Data Analysis	—Unverified
Structure-Enhanced Meta-Learning For Few-Shot Graph Classification	Mar 5, 2021	ClassificationGeneral Classification	CodeCode Available
Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities	Mar 2, 2021	Graph Classification	—Unverified
Graphfool: Targeted Label Adversarial Attack on Graph Embedding	Feb 24, 2021	Adversarial AttackClassification	—Unverified
Generalized Equivariance and Preferential Labeling for GNN Node Classification	Feb 23, 2021	ClassificationGeneral Classification	CodeCode Available
Ego-based Entropy Measures for Structural Representations on Graphs	Feb 17, 2021	Graph Classification	—Unverified
Reinforcement Learning For Data Poisoning on Graph Neural Networks	Feb 12, 2021	BIG-bench Machine LearningClassification	—Unverified

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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