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 576–600 of 927 papers

Title	Date	Tasks	Status	Hype
GraphDIVE: Graph Classification by Mixture of Diverse Experts	Mar 21, 2021	ClassificationGraph Classification	CodeCode Available	0
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models	Mar 19, 2021	DecoderGraph Classification	—Unverified	0
Diversified Multiscale Graph Learning with Graph Self-Correction	Mar 17, 2021	DiversityEnsemble Learning	—Unverified	0
Should Graph Neural Networks Use Features, Edges, Or Both?	Mar 11, 2021	ClassificationGeneral Classification	—Unverified	0
Scaling Up Graph Homomorphism Features with Efficient Data Structures	Mar 8, 2021	Graph Classification	—Unverified	0
Sanity Check for Persistence Diagrams	Mar 8, 2021	Graph ClassificationTopological Data Analysis	—Unverified	0
Size-Invariant Graph Representations for Graph Classification Extrapolations	Mar 8, 2021	ClassificationGeneral Classification	CodeCode Available	1
Structure-Enhanced Meta-Learning For Few-Shot Graph Classification	Mar 5, 2021	ClassificationGeneral Classification	CodeCode Available	0
Graph Autoencoder for Graph Compression and Representation Learning	Mar 4, 2021	Data CompressionGraph Classification	CodeCode Available	1
Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities	Mar 2, 2021	Graph Classification	—Unverified	0
CogDL: A Comprehensive Library for Graph Deep Learning	Mar 1, 2021	Deep LearningGraph Classification	CodeCode Available	2
Graphfool: Targeted Label Adversarial Attack on Graph Embedding	Feb 24, 2021	Adversarial AttackClassification	—Unverified	0
Generalized Equivariance and Preferential Labeling for GNN Node Classification	Feb 23, 2021	ClassificationGeneral Classification	CodeCode Available	0
Accurate Learning of Graph Representations with Graph Multiset Pooling	Feb 23, 2021	Graph ClassificationGraph Clustering	CodeCode Available	1
Ego-based Entropy Measures for Structural Representations on Graphs	Feb 17, 2021	Graph Classification	—Unverified	0
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing	Feb 17, 2021	Graph ClassificationGraph Learning	CodeCode Available	1
Reinforcement Learning For Data Poisoning on Graph Neural Networks	Feb 12, 2021	BIG-bench Machine LearningClassification	—Unverified	0
Online Graph Dictionary Learning	Feb 12, 2021	Dictionary LearningGraph Classification	CodeCode Available	1
Improving Scene Graph Classification by Exploiting Knowledge from Texts	Feb 9, 2021	ClassificationGeneral Classification	—Unverified	0
Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations	Feb 8, 2021	Graph ClassificationGraph Neural Network	CodeCode Available	1
Learning Graph Representations	Feb 3, 2021	General ClassificationGraph Classification	—Unverified	0
Graph Classification Based on Skeleton and Component Features	Feb 2, 2021	ClassificationGeneral Classification	—Unverified	0
[Re] Parameterized Explainer for Graph Neural Network	Jan 31, 2021	Graph ClassificationGraph Neural Network	CodeCode Available	1
Efficient Graph Deep Learning in TensorFlow with tf_geometric	Jan 27, 2021	Deep LearningGeneral Classification	CodeCode Available	1
Identity-aware Graph Neural Networks	Jan 25, 2021	Graph ClassificationGraph Property Prediction	CodeCode Available	2

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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