Node Classification

Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.

Node Classification models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.

( Image credit: Fast Graph Representation Learning With PyTorch Geometric )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1276–1300 of 1860 papers

Title	Date	Tasks	Status	Hype
Explainability-based Backdoor Attacks Against Graph Neural Networks	Apr 8, 2021	Backdoor AttackNode Classification	—Unverified	0
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding	Apr 6, 2021	Link PredictionNetwork Embedding	CodeCode Available	0
New Benchmarks for Learning on Non-Homophilous Graphs	Apr 3, 2021	AttributeFraud Detection	CodeCode Available	1
Modeling Graph Node Correlations with Neighbor Mixture Models	Mar 29, 2021	DenoisingImage Denoising	—Unverified	0
Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs	Mar 26, 2021	Node Classification	—Unverified	0
Bag of Tricks for Node Classification with Graph Neural Networks	Mar 24, 2021	ClassificationGeneral Classification	CodeCode Available	1
Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction	Mar 21, 2021	BenchmarkingClustering	CodeCode Available	0
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs	Mar 17, 2021	BIG-bench Machine LearningGraph Learning	CodeCode Available	1
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks	Mar 16, 2021	ClassificationGeneral Classification	CodeCode Available	1
DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks	Mar 12, 2021	Link PredictionNode Classification	—Unverified	0
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning	Mar 11, 2021	DiversityGraph Embedding	—Unverified	0
Graph Neural Networks Inspired by Classical Iterative Algorithms	Mar 10, 2021	Node Classification	CodeCode Available	1
Scalable Hypergraph Embedding System	Mar 9, 2021	Graph Embeddinghypergraph embedding	—Unverified	0
2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks	Mar 9, 2021	DescriptiveGeneral Classification	—Unverified	0
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks	Mar 8, 2021	Deep AttentionGraph Attention	CodeCode Available	1
Learning Graph Neural Networks with Positive and Unlabeled Nodes	Mar 8, 2021	Graph Neural NetworkNode Classification	—Unverified	0
Unified Robust Training for Graph NeuralNetworks against Label Noise	Mar 5, 2021	image-classificationImage Classification	—Unverified	0
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs	Mar 5, 2021	Bayesian InferenceGeneral Classification	CodeCode Available	0
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs	Mar 4, 2021	Node ClassificationSegmentation	CodeCode Available	1
Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework	Mar 4, 2021	Knowledge DistillationNode Classification	CodeCode Available	1
On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction	Mar 3, 2021	Node Classification	CodeCode Available	0
CogDL: A Comprehensive Library for Graph Deep Learning	Mar 1, 2021	Deep LearningGraph Classification	CodeCode Available	2
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning	Mar 1, 2021	Auxiliary LearningGraph Neural Network	CodeCode Available	1
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data	Feb 27, 2021	Disease PredictionNode Classification	CodeCode Available	1
Graphfool: Targeted Label Adversarial Attack on Graph Embedding	Feb 24, 2021	Adversarial AttackClassification	—Unverified	0

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All datasets Citeseer Pubmed Cora CiteSeer with Public Split: fixed 20 nodes per class PubMed with Public Split: fixed 20 nodes per class Cora with Public Split: fixed 20 nodes per class PPI Coauthor CS Cora (0.5%)Cora (1%)Cora (3%)PubMed (0.03%)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	NodeNet	Accuracy	80.09	—	Unverified
2	SplineCNN	Accuracy	79.2	—	Unverified
3	PathNet	Accuracy (%)	77.98	—	Unverified
4	3ference	Accuracy	76.33	—	Unverified
5	MMA	Accuracy	76.3	—	Unverified
6	PPNP	Accuracy	75.83	—	Unverified
7	CoLinkDist	Accuracy	75.79	—	Unverified
8	CoLinkDistMLP	Accuracy	75.77	—	Unverified
9	APPNP	Accuracy	75.73	—	Unverified
10	Cleora	Accuracy	75.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	NodeNet	Accuracy	90.21	—	Unverified
2	CoLinkDist	Accuracy	89.58	—	Unverified
3	CoLinkDistMLP	Accuracy	89.53	—	Unverified
4	PathNet	Accuracy (%)	88.92	—	Unverified
5	3ference	Accuracy	88.9	—	Unverified
6	SplineCNN	Accuracy	88.88	—	Unverified
7	LinkDist	Accuracy	88.86	—	Unverified
8	LinkDistMLP	Accuracy	88.79	—	Unverified
9	PairE	F1	88.57	—	Unverified
10	GCN + Mixup	Accuracy	87.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LinkDist	Accuracy	88.24	—	Unverified
2	CoLinkDist	Accuracy	87.89	—	Unverified
3	3ference	Accuracy	87.78	—	Unverified
4	LinkDistMLP	Accuracy	87.58	—	Unverified
5	CoLinkDistMLP	Accuracy	87.54	—	Unverified
6	Cleora	Accuracy	86.8	—	Unverified
7	NodeNet	Accuracy	86.8	—	Unverified
8	MMA	Accuracy	85.8	—	Unverified
9	GResNet(GAT)	Accuracy	85.5	—	Unverified
10	DifNet	Accuracy	85.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OGC	Accuracy	77.5	—	Unverified
2	LDS-GNN	Accuracy	75	—	Unverified
3	CPF-tra-APPNP	Accuracy	74.6	—	Unverified
4	G3NN	Accuracy	74.5	—	Unverified
5	GEM	Accuracy	74.2	—	Unverified
6	GGCM	Accuracy	74.2	—	Unverified
7	Truncated Krylov	Accuracy	73.86	—	Unverified
8	SSGC	Accuracy	73.6	—	Unverified
9	OKDEEM	Accuracy	73.53	—	Unverified
10	GCNII	Accuracy	73.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OGC	Accuracy	83.4	—	Unverified
2	CPF-tra-GCNII	Accuracy	83.2	—	Unverified
3	DSGCN	Accuracy	81.9	—	Unverified
4	SuperGAT MX	Accuracy	81.7	—	Unverified
5	Truncated Krylov	Accuracy	81.7	—	Unverified
6	G-APPNP	Accuracy	80.95	—	Unverified
7	GGCM	Accuracy	80.8	—	Unverified
8	GCN(predicted-targets)	Accuracy	80.42	—	Unverified
9	SSGC	Accuracy	80.4	—	Unverified
10	GCNII	Accuracy	80.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OGC	Accuracy	86.9	—	Unverified
2	GCN-TV	Accuracy	86.3	—	Unverified
3	GCNII	Accuracy	85.5	—	Unverified
4	CPF-ind-APPNP	Accuracy	85.3	—	Unverified
5	AIR-GCN	Accuracy	84.7	—	Unverified
6	H-GCN	Accuracy	84.5	—	Unverified
7	G-APPNP	Accuracy	84.31	—	Unverified
8	SuperGAT MX	Accuracy	84.3	—	Unverified
9	DSGCN	Accuracy	84.2	—	Unverified
10	LDS-GNN	Accuracy	84.1	—	Unverified