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 701–725 of 927 papers

Title	Date	Tasks	Status
A graph similarity for deep learning	Dec 1, 2020	Deep LearningGraph Classification	—Unverified
Random Walk Graph Neural Networks	Dec 1, 2020	Graph ClassificationGraph Neural Network	—Unverified
COPT: Coordinated Optimal Transport on Graphs	Dec 1, 2020	Graph Classification	—Unverified
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources	Nov 30, 2020	ClusteringGraph Classification	—Unverified
Classification by Attention: Scene Graph Classification with Prior Knowledge	Nov 19, 2020	ClassificationGeneral Classification	—Unverified
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks	Nov 14, 2020	Auxiliary LearningGraph Classification	—Unverified
Sampling and Recovery of Graph Signals based on Graph Neural Networks	Nov 3, 2020	Graph ClassificationGraph Neural Network	—Unverified
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer	Nov 1, 2020	Graph ClassificationTransfer Learning	—Unverified
ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents	Nov 1, 2020	Graph ClassificationLanguage Modeling	—Unverified
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning	Oct 30, 2020	General ClassificationGraph Classification	CodeCode Available
Graph embedding using multi-layer adjacent point merging model	Oct 28, 2020	Classificationfeature selection	—Unverified
GraphMDN: Leveraging graph structure and deep learning to solve inverse problems	Oct 26, 2020	Graph ClassificationMulti-Hypotheses 3D Human Pose Estimation	—Unverified
Co-embedding of Nodes and Edges with Graph Neural Networks	Oct 25, 2020	BIG-bench Machine LearningGraph Classification	—Unverified
Topology-Aware Graph Pooling Networks	Oct 19, 2020	Graph Classification	—Unverified
K-plex Cover Pooling for Graph Neural Networks	Oct 17, 2020	Graph Classification	—Unverified
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling	Oct 12, 2020	ClassificationDecoder	—Unverified
Towards Expressive Graph Representation	Oct 12, 2020	Graph ClassificationGraph Neural Network	CodeCode Available
Understanding the Power of Persistence Pairing via Permutation Test	Oct 10, 2020	Graph ClassificationTopological Data Analysis	—Unverified
Data-Driven Learning of Geometric Scattering Networks	Oct 6, 2020	DescriptiveGraph Classification	CodeCode Available
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks	Sep 30, 2020	Graph ClassificationLink Prediction	—Unverified
Revisiting Graph Neural Networks for Link Prediction	Sep 28, 2020	ClassificationGraph Classification	—Unverified
GraphCrop: Subgraph Cropping for Graph Classification	Sep 22, 2020	ClassificationData Augmentation	—Unverified
Contrastive Self-supervised Learning for Graph Classification	Sep 13, 2020	ClassificationData Augmentation	—Unverified
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing	Sep 12, 2020	ClassificationGeneral Classification	—Unverified
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization	Aug 28, 2020	Data AugmentationGraph Classification	—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