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 251–275 of 927 papers

Title	Date	Tasks	Status	Score
Evaluating Link Prediction Explanations for Graph Neural Networks	Aug 3, 2023	Graph ClassificationLink Prediction	CodeCode Available	5
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks	May 8, 2024	Graph ClassificationGraph Learning	CodeCode Available	5
Evolution of Graph Classifiers	Oct 4, 2019	Graph Classification	CodeCode Available	5
Efficient Automatic Machine Learning via Design Graphs	Oct 21, 2022	AutoMLGraph Classification	CodeCode Available	5
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness Perspective	Apr 25, 2024	Graph ClassificationGraph Embedding	CodeCode Available	5
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing	Aug 7, 2023	Generalization BoundsGraph Classification	CodeCode Available	5
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs	Apr 18, 2019	Graph ClassificationGraph Neural Network	CodeCode Available	5
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning	Nov 5, 2023	Contrastive LearningGraph Classification	CodeCode Available	5
Cell Attention Networks	Sep 16, 2022	Graph AttentionGraph Classification	CodeCode Available	5
Edge Classification on Graphs: New Directions in Topological Imbalance	Jun 17, 2024	ClassificationEdge Classification	CodeCode Available	5
A Canonicalization Perspective on Invariant and Equivariant Learning	May 28, 2024	Graph ClassificationGraph Embedding	CodeCode Available	5
ImageNet Classification with Deep Convolutional Neural Networks	Dec 1, 2012	General ClassificationGPU	CodeCode Available	5
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs	Aug 17, 2024	Graph ClassificationGraph Neural Network	CodeCode Available	5
Catch Causal Signals from Edges for Label Imbalance in Graph Classification	Jan 3, 2025	Graph Classification	CodeCode Available	5
Identity Inference on Blockchain using Graph Neural Network	Apr 14, 2021	Graph ClassificationGraph Mining	CodeCode Available	5
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings	Dec 10, 2023	Graph ClassificationGraph Regression	CodeCode Available	5
Kernel Graph Convolutional Neural Networks	Oct 29, 2017	General ClassificationGraph Classification	CodeCode Available	5
Generalized Equivariance and Preferential Labeling for GNN Node Classification	Feb 23, 2021	ClassificationGeneral Classification	CodeCode Available	5
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs	Apr 10, 2017	3D Object Classification3D Point Cloud Classification	CodeCode Available	5
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs	Dec 1, 2017	General ClassificationGraph Classification	CodeCode Available	5
Capsule Graph Neural Network	May 1, 2019	Graph ClassificationGraph Neural Network	CodeCode Available	5
How Curvature Enhance the Adaptation Power of Framelet GCNs	Jul 19, 2023	Graph ClassificationGraph Neural Network	CodeCode Available	5
HyperAggregation: Aggregating over Graph Edges with Hypernetworks	Jul 16, 2024	Graph ClassificationGraph Regression	CodeCode Available	5
DOTIN: Dropping Task-Irrelevant Nodes for GNNs	Apr 28, 2022	Graph ClassificationGraph Learning	CodeCode Available	5
DeepGCNs: Can GCNs Go as Deep as CNNs?	Apr 7, 2019	3D Semantic SegmentationGraph Classification	CodeCode Available	5

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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