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 101–125 of 1860 papers

Title	Date	Tasks	Status	Hype
Tailoring Self-Attention for Graph via Rooted Subtrees	Oct 8, 2023	Graph AttentionGraph Learning	CodeCode Available	1
Label-free Node Classification on Graphs with Large Language Models (LLMS)	Oct 7, 2023	Node Classification	CodeCode Available	1
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks	Oct 5, 2023	Graph SamplingNode Classification	CodeCode Available	1
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?	Sep 28, 2023	Graph LearningNode Classification	CodeCode Available	1
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited	Sep 24, 2023	Node Classification	CodeCode Available	1
Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes	Sep 22, 2023	Node ClassificationNode Property Prediction	CodeCode Available	1
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks	Sep 19, 2023	CoLAContrastive Learning	CodeCode Available	1
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark	Sep 1, 2023	Graph ClassificationGraph Learning	CodeCode Available	1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks	Aug 31, 2023	DiversityLink Prediction	CodeCode Available	1
Class-Imbalanced Graph Learning without Class Rebalancing	Aug 27, 2023	ClassificationGraph Learning	CodeCode Available	1
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions	Aug 26, 2023	Graph LearningLink Prediction	CodeCode Available	1
Enhancing Graph Transformers with Hierarchical Distance Structural Encoding	Aug 22, 2023	Graph ClassificationGraph Regression	CodeCode Available	1
Class Label-aware Graph Anomaly Detection	Aug 22, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	1
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity	Aug 18, 2023	Graph ClassificationGraph Representation Learning	CodeCode Available	1
Half-Hop: A graph upsampling approach for slowing down message passing	Aug 17, 2023	Node ClassificationSelf-Supervised Learning	CodeCode Available	1
S-Mixup: Structural Mixup for Graph Neural Networks	Aug 16, 2023	ClassificationGraph Classification	CodeCode Available	1
Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations	Aug 15, 2023	Graph RegressionLink Prediction	CodeCode Available	1
SR-HGN: semantic- and relation-aware heterogeneous graph neural network	Aug 15, 2023	Graph Neural NetworkNode Classification	CodeCode Available	1
SimMatchV2: Semi-Supervised Learning with Graph Consistency	Aug 13, 2023	image-classificationImage Classification	CodeCode Available	1
UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification	Aug 3, 2023	Node ClassificationRepresentation Learning	CodeCode Available	1
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning	Aug 3, 2023	Feature EngineeringGraph Learning	CodeCode Available	1
OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning	Jul 21, 2023	Domain AdaptationGRAPH DOMAIN ADAPTATION	CodeCode Available	1
PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds	Jul 17, 2023	Graph Neural NetworkNode Classification	CodeCode Available	1
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models	Jul 17, 2023	BenchmarkingGraph Clustering	CodeCode Available	1
Long-range Meta-path Search on Large-scale Heterogeneous Graphs	Jul 17, 2023	Node ClassificationNode Property Prediction	CodeCode Available	1

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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	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