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
Mutual Information Maximization in Graph Neural Networks	May 21, 2019	General ClassificationGraph Classification	CodeCode Available
IPC: A Benchmark Data Set for Learning with Graph-Structured Data	May 15, 2019	BenchmarkingGraph Classification	CodeCode Available
Function Space Pooling For Graph Convolutional Networks	May 15, 2019	General ClassificationGraph Classification	—Unverified
On Graph Classification Networks, Datasets and Baselines	May 12, 2019	BIG-bench Machine LearningClassification	—Unverified
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification	May 11, 2019	ClassificationGeneral Classification	CodeCode Available
Graph U-Nets	May 11, 2019	DecoderGeneral Classification	CodeCode Available
Understanding Attention and Generalization in Graph Neural Networks	May 8, 2019	Graph Classification	CodeCode Available
PiNet: A Permutation Invariant Graph Neural Network for Graph Classification	May 8, 2019	ClassificationGeneral Classification	CodeCode Available
Learning Graph Neural Networks with Noisy Labels	May 5, 2019	General ClassificationGraph Classification	—Unverified
Capsule Graph Neural Network	May 1, 2019	Graph ClassificationGraph Neural Network	CodeCode Available
Graph Classification with Geometric Scattering	May 1, 2019	ClassificationGeneral Classification	—Unverified
Graph Transformer	May 1, 2019	Few-Shot LearningGeneral Classification	—Unverified
DEEP GEOMETRICAL GRAPH CLASSIFICATION	May 1, 2019	ClassificationClustering	—Unverified
Graph Convolutional Networks with EigenPooling	Apr 30, 2019	ClassificationGeneral Classification	CodeCode Available
Graph Kernels: A Survey	Apr 27, 2019	Graph ClassificationSurvey	—Unverified
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels	Apr 21, 2019	Feature EngineeringGraph Attention	CodeCode Available
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures	Apr 20, 2019	Graph ClassificationTopological Data Analysis	CodeCode Available
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs	Apr 18, 2019	Graph ClassificationGraph Neural Network	CodeCode Available
Self-Attention Graph Pooling	Apr 17, 2019	Graph Classification	CodeCode Available
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective	Apr 10, 2019	ClassificationGeneral Classification	CodeCode Available
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network	Apr 8, 2019	BIG-bench Machine LearningDeep Learning	—Unverified
DeepGCNs: Can GCNs Go as Deep as CNNs?	Apr 7, 2019	3D Semantic SegmentationGraph Classification	CodeCode Available
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification	Apr 6, 2019	General ClassificationGraph Classification	—Unverified
DAGCN: Dual Attention Graph Convolutional Networks	Apr 4, 2019	General ClassificationGraph Classification	CodeCode Available
Rep the Set: Neural Networks for Learning Set Representations	Apr 3, 2019	General ClassificationGraph Classification	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	δ-2-LWL	Accuracy	85.5	—	Unverified
9	DUGNN	Accuracy	85.5	—	Unverified
10	CIN++	Accuracy	85.3	—	Unverified