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 776–800 of 927 papers

Title	Date	Tasks	Status
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning	Oct 26, 2019	Bayesian InferenceGeneral Classification	—Unverified
Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction	Oct 24, 2019	General ClassificationGraph Classification	—Unverified
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling	Oct 24, 2019	Graph ClassificationRepresentation Learning	CodeCode Available
Temporal Graph Kernels for Classifying Dissemination Processes	Oct 14, 2019	General ClassificationGraph Classification	—Unverified
Rethinking Kernel Methods for Node Representation Learning on Graphs	Oct 6, 2019	ClassificationGeneral Classification	CodeCode Available
Evolution of Graph Classifiers	Oct 4, 2019	Graph Classification	CodeCode Available
Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection	Oct 2, 2019	General ClassificationGraph Classification	—Unverified
AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism	Oct 1, 2019	General ClassificationGraph Classification	CodeCode Available
Universal Graph Transformer Self-Attention Networks	Sep 26, 2019	General ClassificationGraph Classification	CodeCode Available
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs	Sep 26, 2019	Data AugmentationGeneral Classification	—Unverified
Ordinary differential equations on graph networks	Sep 25, 2019	Graph ClassificationGraph Neural Network	—Unverified
How can we generalise learning distributed representations of graphs?	Sep 25, 2019	Binary ClassificationClassification	—Unverified
HaarPooling: Graph Pooling with Compressive Haar Basis	Sep 25, 2019	Graph Classification	—Unverified
Haar Graph Pooling	Sep 25, 2019	General ClassificationGraph Classification	CodeCode Available
Diagonal Graph Convolutional Networks with Adaptive Neighborhood Aggregation	Sep 25, 2019	Graph AttentionGraph Classification	—Unverified
Unsupervised Universal Self-Attention Network for Graph Classification	Sep 25, 2019	ClassificationGraph Classification	—Unverified
Subgraph Attention for Node Classification and Hierarchical Graph Pooling	Sep 25, 2019	ClassificationGraph Classification	—Unverified
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention	Sep 25, 2019	Graph ClassificationGraph Neural Network	—Unverified
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes	Sep 25, 2019	Graph ClassificationGraph Representation Learning	—Unverified
Topology-Aware Pooling via Graph Attention	Sep 25, 2019	Graph AttentionGraph Classification	—Unverified
Mincut Pooling in Graph Neural Networks	Sep 25, 2019	Graph Classification	—Unverified
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning	Sep 22, 2019	DecoderGraph Classification	CodeCode Available
A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks	Sep 7, 2019	Graph Classification	—Unverified
NEAR: Neighborhood Edge AggregatoR for Graph Classification	Sep 6, 2019	ClassificationGeneral Classification	—Unverified
Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning	Aug 19, 2019	General ClassificationGraph Classification	—Unverified

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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