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 901–925 of 1860 papers

Title	Date	Tasks	Status	Hype
Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos	Jul 13, 2022	Change DetectionDeep Learning	—Unverified	0
The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings	Jul 13, 2022	Graph EmbeddingKnowledge Graph Embedding	CodeCode Available	1
From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks	Jul 12, 2022	Node Classification	—Unverified	0
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
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods	Jul 10, 2022	AttributeFairness	—Unverified	0
Graph Generative Model for Benchmarking Graph Neural Networks	Jul 10, 2022	BenchmarkingGraph Generation	CodeCode Available	1
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling	Jul 7, 2022	Node Classification	—Unverified	0
TREE-G: Decision Trees Contesting Graph Neural Networks	Jul 6, 2022	Graph ClassificationGraph Learning	CodeCode Available	1
What Do Graph Convolutional Neural Networks Learn?	Jul 5, 2022	Node Classification	CodeCode Available	0
Neural Networks in a Product of Hyperbolic Spaces	Jul 1, 2022	Graph EmbeddingKnowledge Graph Embedding	—Unverified	0
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning	Jul 1, 2022	Few-Shot LearningLink Prediction	—Unverified	0
Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality	Jul 1, 2022	Graph EmbeddingKnowledge Graph Embedding	CodeCode Available	0
Modularity Optimization as a Training Criterion for Graph Neural Networks	Jun 30, 2022	AttributeNode Classification	CodeCode Available	0
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
Geometry Contrastive Learning on Heterogeneous Graphs	Jun 25, 2022	Contrastive LearningNode Classification	CodeCode Available	0
Task-Adaptive Few-shot Node Classification	Jun 23, 2022	ClassificationFew-Shot Learning	CodeCode Available	1
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training	Jun 23, 2022	Contrastive LearningGraph Classification	—Unverified	0
Understanding convolution on graphs via energies	Jun 22, 2022	Inductive BiasNode Classification	CodeCode Available	1
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks	Jun 22, 2022	Bilevel OptimizationClassification	CodeCode Available	0
Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks	Jun 21, 2022	AttributeNode Classification	—Unverified	0
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks	Jun 20, 2022	Node Classification	CodeCode Available	1
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs	Jun 18, 2022	AttributeNode Classification	CodeCode Available	0
DFG-NAS: Deep and Flexible Graph Neural Architecture Search	Jun 17, 2022	Neural Architecture SearchNode Classification	CodeCode Available	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	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