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 876–900 of 927 papers

Title	Date	Tasks	Status	Hype
Anonymous Walk Embeddings	May 30, 2018	General ClassificationGraph Classification	CodeCode Available	1
Optimal Transport for structured data with application on graphs	May 23, 2018	ClusteringGraph Classification	CodeCode Available	2
Graph Capsule Convolutional Neural Networks	May 21, 2018	Deep LearningGeneral Classification	CodeCode Available	0
Learning Graph-Level Representations with Recurrent Neural Networks	May 20, 2018	General ClassificationGraph Classification	CodeCode Available	0
Change Point Methods on a Sequence of Graphs	May 18, 2018	Graph ClassificationSeizure Detection	—Unverified	0
An End-to-End Deep Learning Architecture for Graph Classification	Apr 29, 2018	Deep LearningGeneral Classification	CodeCode Available	0
Walk-Steered Convolution for Graph Classification	Apr 16, 2018	ClassificationClustering	—Unverified	0
Kernel Graph Convolutional Neural Nets	Jan 1, 2018	Graph Classification	—Unverified	0
DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model	Dec 10, 2017	Graph ClassificationGraph Similarity	—Unverified	0
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs	Dec 1, 2017	General ClassificationGraph Classification	CodeCode Available	0
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels	Nov 24, 2017	Deep LearningGeneral Classification	CodeCode Available	3
Residual Gated Graph ConvNets	Nov 20, 2017	ClusteringGeneral Classification	CodeCode Available	0
Graph Attention Networks	Oct 30, 2017	Document ClassificationGraph Attention	CodeCode Available	1
Kernel Graph Convolutional Neural Networks	Oct 29, 2017	General ClassificationGraph Classification	CodeCode Available	0
Deep Graph Attention Model	Sep 15, 2017	General ClassificationGraph Attention	—Unverified	0
Learning Universal Adversarial Perturbations with Generative Models	Aug 17, 2017	Graph Classification	CodeCode Available	0
Graph Classification via Deep Learning with Virtual Nodes	Aug 14, 2017	ClassificationDeep Learning	—Unverified	0
Graph Classification with 2D Convolutional Neural Networks	Jul 29, 2017	ClassificationGeneral Classification	—Unverified	0
graph2vec: Learning Distributed Representations of Graphs	Jul 17, 2017	ClusteringGeneral Classification	CodeCode Available	1
Kernel method for persistence diagrams via kernel embedding and weight factor	Jun 12, 2017	Graph ClassificationTopological Data Analysis	CodeCode Available	0
Sliced Wasserstein Kernel for Persistence Diagrams	Jun 11, 2017	Graph ClassificationTopological Data Analysis	—Unverified	0
Inductive Representation Learning on Large Graphs	Jun 7, 2017	Graph ClassificationGraph Regression	CodeCode Available	1
Dynamics Based Features For Graph Classification	May 30, 2017	ClassificationGeneral Classification	—Unverified	0
Supervised Community Detection with Line Graph Neural Networks	May 23, 2017	Community DetectionGraph Classification	CodeCode Available	0
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs	Apr 10, 2017	3D Object Classification3D Point Cloud Classification	CodeCode Available	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