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 176–200 of 1860 papers

Title	Date	Tasks	Status	Hype
Binary Graph Convolutional Network with Capacity Exploration	Oct 24, 2022	BinarizationNode Classification	CodeCode Available	1
MGNNI: Multiscale Graph Neural Networks with Implicit Layers	Oct 15, 2022	Graph ClassificationGraph Neural Network	CodeCode Available	1
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking	Oct 14, 2022	BenchmarkingGPU	CodeCode Available	1
Revisiting Heterophily For Graph Neural Networks	Oct 14, 2022	Inductive BiasNode Classification	CodeCode Available	1
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization	Oct 5, 2022	Link PredictionNode Classification	CodeCode Available	1
Spectral Augmentation for Self-Supervised Learning on Graphs	Oct 2, 2022	Contrastive LearningNode Classification	CodeCode Available	1
Gradient Gating for Deep Multi-Rate Learning on Graphs	Oct 2, 2022	Graph Learning	CodeCode Available	1
MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization	Sep 30, 2022	Link PredictionNode Classification	CodeCode Available	1
Structure-Preserving Graph Representation Learning	Sep 2, 2022	Graph Representation LearningNode Classification	CodeCode Available	1
Heterogeneous Graph Tree Networks	Sep 1, 2022	Node Classification	CodeCode Available	1
LTE4G: Long-Tail Experts for Graph Neural Networks	Aug 22, 2022	Knowledge DistillationNode Classification	CodeCode Available	1
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks	Aug 15, 2022	Graph Representation LearningNode Classification	CodeCode Available	1
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks	Aug 14, 2022	Few-Shot LearningNode Classification	CodeCode Available	1
Multiplex Heterogeneous Graph Convolutional Network	Aug 12, 2022	AttributeLink Prediction	CodeCode Available	1
Generative Subgraph Contrast for Self-Supervised Graph Representation Learning	Jul 25, 2022	Contrastive LearningGraph Representation Learning	CodeCode Available	1
Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network	Jul 20, 2022	Graph MiningGraph Neural Network	CodeCode Available	1
Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection	Jul 15, 2022	Active Speaker DetectionAudio-Visual Active Speaker Detection	CodeCode Available	1
Equivariant Hypergraph Diffusion Neural Operators	Jul 14, 2022	Computational EfficiencyNode Classification	CodeCode Available	1
The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings	Jul 13, 2022	Graph EmbeddingKnowledge Graph Embedding	CodeCode Available	1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEM	Jul 11, 2022	Graph Representation LearningNode Classification	CodeCode Available	1
Graph Generative Model for Benchmarking Graph Neural Networks	Jul 10, 2022	BenchmarkingGraph Generation	CodeCode Available	1
TREE-G: Decision Trees Contesting Graph Neural Networks	Jul 6, 2022	Graph ClassificationGraph Learning	CodeCode Available	1
A Representation Learning Framework for Property Graphs	Jun 27, 2022	Graph EmbeddingGraph Representation Learning	CodeCode Available	1
Structural Entropy Guided Graph Hierarchical Pooling	Jun 26, 2022	Classificationglobal-optimization	CodeCode Available	1
TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification	Jun 26, 2022	Node Classification	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