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 676–700 of 1860 papers

Title	Date	Tasks	Status	Hype
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs	May 22, 2023	Data AugmentationGraph Representation Learning	—Unverified	0
Self-Explainable Graph Neural Networks for Link Prediction	May 21, 2023	Link PredictionNode Classification	—Unverified	0
Graph Propagation Transformer for Graph Representation Learning	May 19, 2023	Graph LearningGraph Property Prediction	CodeCode Available	1
Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain	May 18, 2023	Graph Neural NetworkNode Classification	—Unverified	0
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs	May 18, 2023	Dynamic Node ClassificationNode Classification	CodeCode Available	0
Edge Directionality Improves Learning on Heterophilic Graphs	May 17, 2023	Graph Neural NetworkNode Classification	CodeCode Available	1
Optimality of Message-Passing Architectures for Sparse Graphs	May 17, 2023	Graph Neural NetworkNode Classification	—Unverified	0
Addressing Heterophily in Node Classification with Graph Echo State Networks	May 14, 2023	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	CodeCode Available	0
DRew: Dynamically Rewired Message Passing with Delay	May 13, 2023	Graph ClassificationGraph Regression	CodeCode Available	1
Fisher Information Embedding for Node and Graph Learning	May 12, 2023	Graph AttentionGraph Learning	CodeCode Available	1
Feature Expansion for Graph Neural Networks	May 10, 2023	Node ClassificationRepresentation Learning	CodeCode Available	1
Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module	May 9, 2023	Node Classification	CodeCode Available	0
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning	May 8, 2023	AttributeContrastive Learning	CodeCode Available	1
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity	May 7, 2023	Graph Neural NetworkNode Classification	CodeCode Available	1
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning	May 5, 2023	AttributeContrastive Learning	—Unverified	0
Zoo Guide to Network Embedding	May 5, 2023	Community DetectionLink Prediction	—Unverified	0
PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning	May 4, 2023	ArticlesGraph Mining	CodeCode Available	0
A novel measure to identify influential nodes: Return Random Walk Gravity Centrality	May 1, 2023	Approximating Betweenness-Centrality rankingNode Classification	CodeCode Available	0
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks	Apr 29, 2023	Node Classification	CodeCode Available	0
Imbalanced Node Classification Beyond Homophilic Assumption	Apr 28, 2023	ClassificationNode Classification	—Unverified	0
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability	Apr 25, 2023	Node ClassificationStochastic Block Model	CodeCode Available	1
Connector 0.5: A unified framework for graph representation learning	Apr 25, 2023	Graph EmbeddingGraph Generation	CodeCode Available	0
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness	Apr 24, 2023	Graph EmbeddingLink Prediction	—Unverified	0
Meta-multigraph Search: Rethinking Meta-structure on Heterogeneous Information Networks	Apr 23, 2023	Node Classification	—Unverified	0
Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach	Apr 22, 2023	Decision MakingGraph Neural Network	—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	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