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 826–850 of 927 papers

Title	Date	Tasks	Status	Hype
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs	Apr 18, 2019	Graph ClassificationGraph Neural Network	CodeCode Available	0
Self-Attention Graph Pooling	Apr 17, 2019	Graph Classification	CodeCode Available	0
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective	Apr 10, 2019	ClassificationGeneral Classification	CodeCode Available	0
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network	Apr 8, 2019	BIG-bench Machine LearningDeep Learning	—Unverified	0
DeepGCNs: Can GCNs Go as Deep as CNNs?	Apr 7, 2019	3D Semantic SegmentationGraph Classification	CodeCode Available	0
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification	Apr 6, 2019	General ClassificationGraph Classification	—Unverified	0
DAGCN: Dual Attention Graph Convolutional Networks	Apr 4, 2019	General ClassificationGraph Classification	CodeCode Available	0
Rep the Set: Neural Networks for Learning Set Representations	Apr 3, 2019	General ClassificationGraph Classification	CodeCode Available	0
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings	Apr 2, 2019	General ClassificationGraph Classification	CodeCode Available	0
Quantum-based subgraph convolutional neural networks	Apr 1, 2019	General ClassificationGraph Classification	—Unverified	0
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity	Apr 1, 2019	General ClassificationGraph Classification	CodeCode Available	0
Clique pooling for graph classification	Mar 31, 2019	ClassificationGeneral Classification	CodeCode Available	0
A Survey on Graph Kernels	Mar 28, 2019	ClassificationGeneral Classification	—Unverified	0
Subgraph Networks with Application to Structural Feature Space Expansion	Mar 21, 2019	General ClassificationGraph Classification	—Unverified	0
GNNExplainer: Generating Explanations for Graph Neural Networks	Mar 10, 2019	BIG-bench Machine LearningExplainable artificial intelligence	CodeCode Available	1
Relational Pooling for Graph Representations	Mar 6, 2019	General ClassificationGraph Classification	CodeCode Available	0
Fast Graph Representation Learning with PyTorch Geometric	Mar 6, 2019	GPUGraph Classification	CodeCode Available	1
Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons	Mar 1, 2019	BIG-bench Machine LearningGraph Classification	CodeCode Available	0
Learning Vertex Convolutional Networks for Graph Classification	Feb 26, 2019	ClassificationGeneral Classification	—Unverified	0
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations	Feb 22, 2019	BenchmarkingClassification	—Unverified	0
Fake News Detection on Social Media using Geometric Deep Learning	Feb 10, 2019	Common Sense ReasoningDeep Learning	CodeCode Available	1
Variational Recurrent Neural Networks for Graph Classification	Feb 7, 2019	ClassificationGeneral Classification	CodeCode Available	1
Propagation kernels: efficient graph kernels from propagated information	Feb 1, 2019	Graph Classification	CodeCode Available	0
Graph Neural Networks with convolutional ARMA filters	Jan 5, 2019	ClassificationGeneral Classification	CodeCode Available	0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification	Dec 10, 2018	Graph ClassificationGraph Embedding	—Unverified	0

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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