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	Hype
Factorizable Graph Convolutional Networks	Oct 12, 2020	Graph ClassificationGraph Regression	CodeCode Available	1
Towards Expressive Graph Representation	Oct 12, 2020	Graph ClassificationGraph Neural Network	CodeCode Available	0
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling	Oct 12, 2020	ClassificationDecoder	—Unverified	0
Graph Information Bottleneck for Subgraph Recognition	Oct 12, 2020	DenoisingGraph Classification	CodeCode Available	1
Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations	Oct 12, 2020	ClassificationDecoder	CodeCode Available	1
Understanding the Power of Persistence Pairing via Permutation Test	Oct 10, 2020	Graph ClassificationTopological Data Analysis	—Unverified	0
Directional Graph Networks	Oct 6, 2020	Data AugmentationGraph Classification	CodeCode Available	1
Data-Driven Learning of Geometric Scattering Networks	Oct 6, 2020	DescriptiveGraph Classification	CodeCode Available	0
Graph Cross Networks with Vertex Infomax Pooling	Oct 5, 2020	ClassificationGeneral Classification	CodeCode Available	1
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks	Sep 30, 2020	Graph ClassificationLink Prediction	—Unverified	0
Revisiting Graph Neural Networks for Link Prediction	Sep 28, 2020	ClassificationGraph Classification	—Unverified	0
Learning Graph Normalization for Graph Neural Networks	Sep 24, 2020	Graph ClassificationGraph Regression	CodeCode Available	1
GraphCrop: Subgraph Cropping for Graph Classification	Sep 22, 2020	ClassificationData Augmentation	—Unverified	0
Contrastive Self-supervised Learning for Graph Classification	Sep 13, 2020	ClassificationData Augmentation	—Unverified	0
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing	Sep 12, 2020	ClassificationGeneral Classification	—Unverified	0
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training	Sep 7, 2020	Graph ClassificationGraph Neural Network	CodeCode Available	1
Lifelong Graph Learning	Sep 1, 2020	Action RecognitionContinual Learning	CodeCode Available	1
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization	Aug 28, 2020	Data AugmentationGraph Classification	—Unverified	0
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks	Aug 20, 2020	Graph ClassificationNode Classification	—Unverified	0
Quaternion Graph Neural Networks	Aug 12, 2020	General ClassificationGraph Classification	CodeCode Available	1
PiNet: Attention Pooling for Graph Classification	Aug 11, 2020	ClassificationGeneral Classification	CodeCode Available	0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks	Aug 11, 2020	CPUGraph Classification	—Unverified	0
Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription	Aug 4, 2020	Graph ClassificationMetric Learning	—Unverified	0
Detecting Beneficial Feature Interactions for Recommender Systems	Aug 2, 2020	Graph ClassificationGraph Neural Network	CodeCode Available	1
Multi-view adaptive graph convolutions for graph classification	Jul 24, 2020	ClassificationGeneral Classification	—Unverified	0

Show:10 25 50

← PrevPage 27 of 38Next →

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