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 751–775 of 927 papers

Title	Date	Tasks	Status
Learning Deep Graph Representations via Convolutional Neural Networks	Apr 5, 2020	General ClassificationGraph Classification	CodeCode Available
A Collective Learning Framework to Boost GNN Expressiveness	Mar 26, 2020	ClassificationGeneral Classification	—Unverified
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay	Mar 22, 2020	Graph ClassificationGraph Learning	—Unverified
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification	Mar 18, 2020	ClassificationFew-Shot Learning	—Unverified
Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization	Mar 18, 2020	General ClassificationGraph Classification	CodeCode Available
Wasserstein-based Graph Alignment	Mar 12, 2020	Graph Classification	—Unverified
Ego-based Entropy Measures for Structural Representations	Mar 1, 2020	General ClassificationGraph Classification	—Unverified
An End-to-End Graph Convolutional Kernel Support Vector Machine	Feb 29, 2020	ClassificationFeature Engineering	—Unverified
Distance Metric Learning for Graph Structured Data	Feb 3, 2020	Computational EfficiencyGeneral Classification	CodeCode Available
GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features	Jan 23, 2020	General ClassificationGraph Classification	—Unverified
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction	Jan 16, 2020	General ClassificationGraph Classification	—Unverified
Understanding the Power of Persistence Pairing via Permutation Test	Jan 16, 2020	General ClassificationGraph Classification	—Unverified
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport	Dec 24, 2019	Graph Classification	CodeCode Available
Multi-Channel Graph Convolutional Networks	Dec 17, 2019	ClusteringGraph Classification	—Unverified
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis	Dec 16, 2019	ClassificationGeneral Classification	—Unverified
Coloring graph neural networks for node disambiguation	Dec 12, 2019	Graph ClassificationGraph Neural Network	—Unverified
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax	Dec 11, 2019	Bayesian OptimizationGaussian Processes	—Unverified
Sparse Graph Attention Networks	Dec 2, 2019	ClassificationGeneral Classification	CodeCode Available
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks	Nov 27, 2019	Graph ClassificationRecommendation Systems	CodeCode Available
Independence Promoted Graph Disentangled Networks	Nov 26, 2019	ClusteringGraph Classification	—Unverified
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding	Nov 25, 2019	Graph ClassificationGraph Embedding	—Unverified
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations	Nov 18, 2019	ClassificationGeneral Classification	CodeCode Available
A Hierarchy of Graph Neural Networks Based on Learnable Local Features	Nov 13, 2019	General ClassificationGraph Classification	—Unverified
Fundamental Limits of Deep Graph Convolutional Networks	Oct 28, 2019	Graph ClassificationGraph Representation Learning	—Unverified
Understanding Isomorphism Bias in Graph Data Sets	Oct 26, 2019	General ClassificationGraph Classification	CodeCode Available

Show:10 25 50

← PrevPage 31 of 38Next →

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