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 901–927 of 927 papers

Title	Date	Tasks	Status
Sliced Wasserstein Kernel for Persistence Diagrams	Jun 11, 2017	Graph ClassificationTopological Data Analysis	—Unverified
Dynamics Based Features For Graph Classification	May 30, 2017	ClassificationGeneral Classification	—Unverified
Supervised Community Detection with Line Graph Neural Networks	May 23, 2017	Community DetectionGraph Classification	CodeCode Available
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs	Apr 10, 2017	3D Object Classification3D Point Cloud Classification	CodeCode Available
Shift Aggregate Extract Networks	Mar 16, 2017	General ClassificationGraph Classification	—Unverified
Global Weisfeiler-Lehman Graph Kernels	Mar 7, 2017	General ClassificationGraph Classification	CodeCode Available
Network classification with applications to brain connectomics	Jan 27, 2017	ClassificationFunctional Connectivity	CodeCode Available
Geometric deep learning on graphs and manifolds using mixture model CNNs	Nov 25, 2016	Deep LearningDocument Classification	CodeCode Available
On Valid Optimal Assignment Kernels and Applications to Graph Classification	Jun 3, 2016	ClassificationGeneral Classification	—Unverified
Structural Deep Network Embedding	Jun 1, 2016	Graph ClassificationLink Prediction	CodeCode Available
Learning Convolutional Neural Networks for Graphs	May 17, 2016	Graph Classification	CodeCode Available
The Multiscale Laplacian Graph Kernel	Mar 20, 2016	Graph Classification	—Unverified
Halting in Random Walk Kernels	Dec 1, 2015	General ClassificationGraph Classification	—Unverified
Generalized Shortest Path Kernel on Graphs	Oct 22, 2015	General ClassificationGraph Classification	—Unverified
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification	Aug 19, 2015	Diagnosticfeature selection	—Unverified
Deep Graph Kernels	Aug 10, 2015	Graph ClassificationLanguage Modeling	—Unverified
Graph Invariant Kernels	Jul 25, 2015	Graph Classification	—Unverified
Persistence Images: A Stable Vector Representation of Persistent Homology	Jul 22, 2015	BIG-bench Machine LearningGraph Classification	CodeCode Available
Extending local features with contextual information in graph kernels	Jul 8, 2015	Graph Classification	—Unverified
Text Categorization as a Graph Classification Problem	Jul 1, 2015	ClassificationGeneral Classification	—Unverified
Graphs in machine learning: an introduction	Jun 23, 2015	BIG-bench Machine LearningClustering	—Unverified
Graphlet-based lazy associative graph classification	Apr 21, 2015	ClassificationGeneral Classification	—Unverified
Graph Kernels via Functional Embedding	Apr 21, 2014	General ClassificationGraph Classification	—Unverified
Planning by case-based reasoning based on fuzzy logic	Nov 18, 2013	Graph ClassificationRetrieval	—Unverified
ImageNet Classification with Deep Convolutional Neural Networks	Dec 1, 2012	General ClassificationGPU	CodeCode Available
Efficient graphlet kernels for large graph comparison	Apr 16, 2009	Graph Classification	—Unverified
Distinguishing Enzyme Structures from Non-enzymes Without Alignments	Jul 18, 2003	Graph 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