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 1251–1275 of 1860 papers

Title	Date	Tasks	Status	Hype
Graph Sanitation with Application to Node Classification	May 19, 2021	Anomaly DetectionBilevel Optimization	—Unverified	0
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks	May 18, 2021	Node ClassificationNode Property Prediction	CodeCode Available	1
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective	May 17, 2021	Link PredictionNetwork Embedding	—Unverified	0
Improving Graph Neural Networks with Simple Architecture Design	May 17, 2021	feature selectionGraph Neural Network	CodeCode Available	1
Neural Trees for Learning on Graphs	May 15, 2021	Node Classification	—Unverified	0
Meta-Inductive Node Classification across Graphs	May 14, 2021	ClassificationGeneral Knowledge	—Unverified	0
GIPA: General Information Propagation Algorithm for Graph Learning	May 13, 2021	Graph AttentionGraph Learning	CodeCode Available	1
The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs	May 12, 2021	BIG-bench Machine LearningNode Classification	—Unverified	0
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning	May 10, 2021	CPUGPU	CodeCode Available	1
Graph Attention Networks with Positional Embeddings	May 9, 2021	Graph AttentionNode Classification	—Unverified	0
Non-Recursive Graph Convolutional Networks	May 9, 2021	Node ClassificationRepresentation Learning	—Unverified	0
Diffusion Mechanism in Residual Neural Network: Theory and Applications	May 7, 2021	Binary ClassificationClassification	CodeCode Available	1
CrossWalk: Fairness-enhanced Node Representation Learning	May 6, 2021	FairnessLink Prediction	CodeCode Available	1
VersaGNN: a Versatile accelerator for Graph neural networks	May 4, 2021	CPUGPU	—Unverified	0
WGCN: Graph Convolutional Networks with Weighted Structural Features	Apr 29, 2021	Node Classification	CodeCode Available	1
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification	Apr 28, 2021	General ClassificationGraph Learning	—Unverified	0
LGD-GCN: Local and Global Disentangled Graph Convolutional Networks	Apr 24, 2021	DisentanglementDiversity	CodeCode Available	1
Seastar: vertex-centric programming for graph neural networks	Apr 21, 2021	GPUGraph Clustering	—Unverified	0
Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection	Apr 19, 2021	Fraud DetectionGraph Neural Network	CodeCode Available	1
SAS: A Simple, Accurate and Scalable Node Classification Algorithm	Apr 19, 2021	ClassificationGeneral Classification	—Unverified	0
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks	Apr 16, 2021	AttributeClustering	CodeCode Available	1
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks	Apr 16, 2021	Fraud DetectionGraph Neural Network	CodeCode Available	1
A Hyperbolic-to-Hyperbolic Graph Convolutional Network	Apr 14, 2021	General ClassificationGraph Classification	—Unverified	0
Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood	Apr 12, 2021	AttributeDecoder	—Unverified	0
Edge: Enriching Knowledge Graph Embeddings with External Text	Apr 11, 2021	Knowledge Graph EmbeddingsKnowledge Graphs	—Unverified	0

Show:10 25 50

← PrevPage 51 of 75Next →

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	GGCM	Accuracy	74.2	—	Unverified
6	GEM	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