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 401–425 of 927 papers

Title	Date	Tasks	Status
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning	Dec 9, 2023	Graph ClassificationGraph Learning	—Unverified
On the Initialization of Graph Neural Networks	Dec 5, 2023	Graph ClassificationGraph Representation Learning	CodeCode Available
On the Adversarial Robustness of Graph Contrastive Learning Methods	Nov 29, 2023	Adversarial RobustnessContrastive Learning	—Unverified
Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal	Nov 24, 2023	AllClustering	—Unverified
Hard Label Black Box Node Injection Attack on Graph Neural Networks	Nov 22, 2023	ClassificationGraph Classification	CodeCode Available
Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning	Nov 22, 2023	BenchmarkingDrug Discovery	—Unverified
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study	Nov 21, 2023	Data AugmentationFew-Shot Learning	—Unverified
GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps	Nov 10, 2023	Anomaly DetectionDecision Making	—Unverified
Going beyond persistent homology using persistent homology	Nov 10, 2023	Graph Classification	—Unverified
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning	Nov 5, 2023	Contrastive LearningGraph Classification	CodeCode Available
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning	Oct 31, 2023	Graph ClassificationGraph Generation	—Unverified
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding	Oct 28, 2023	Graph ClassificationGraph Embedding	CodeCode Available
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks	Oct 26, 2023	Contrastive LearningGraph Classification	CodeCode Available
BLIS-Net: Classifying and Analyzing Signals on Graphs	Oct 26, 2023	ClassificationGraph Classification	—Unverified
A Causal Disentangled Multi-Granularity Graph Classification Method	Oct 25, 2023	ClassificationDisentanglement	—Unverified
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning	Oct 24, 2023	Contrastive LearningGraph Classification	—Unverified
Self-supervision meets kernel graph neural models: From architecture to augmentations	Oct 17, 2023	Data AugmentationGraph Classification	—Unverified
Mirage: Model-Agnostic Graph Distillation for Graph Classification	Oct 14, 2023	BenchmarkingClassification	CodeCode Available
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation	Oct 10, 2023	Graph ClassificationGraph Embedding	CodeCode Available
Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose	Oct 10, 2023	Graph Classification	—Unverified
GDM: Dual Mixup for Graph Classification with Limited Supervision	Sep 18, 2023	DiversityGraph Classification	—Unverified
Recovering Missing Node Features with Local Structure-based Embeddings	Sep 16, 2023	Graph Classification	—Unverified
Graph Neural Networks Use Graphs When They Shouldn't	Sep 8, 2023	Graph Classification	CodeCode Available
Filtration Surfaces for Dynamic Graph Classification	Sep 7, 2023	ClassificationGraph Classification	CodeCode Available
Generalized Simplicial Attention Neural Networks	Sep 5, 2023	Graph ClassificationImputation	CodeCode Available

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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