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 1226–1250 of 1860 papers

Title	Date	Tasks	Status	Hype
Multiple Kernel Representation Learning on Networks	Jun 9, 2021	Link PredictionNode Classification	CodeCode Available	0
XBNet : An Extremely Boosted Neural Network	Jun 9, 2021	Anomaly DetectionBreast Cancer Detection	CodeCode Available	1
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs	Jun 8, 2021	Graph Neural NetworkNode Classification	CodeCode Available	1
Graph-MLP: Node Classification without Message Passing in Graph	Jun 8, 2021	ClassificationGraph Neural Network	CodeCode Available	1
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs	Jun 7, 2021	Graph AttentionGraph Embedding	CodeCode Available	0
Pseudo-Riemannian Graph Convolutional Networks	Jun 6, 2021	Graph ReconstructionInductive Bias	CodeCode Available	1
Graph Belief Propagation Networks	Jun 6, 2021	ClassificationGraph Neural Network	CodeCode Available	0
On Local Aggregation in Heterophilic Graphs	Jun 6, 2021	Node Classification	—Unverified	0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data	Jun 5, 2021	Causal InferenceGraph Learning	—Unverified	0
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks	Jun 5, 2021	AttributeClassification	CodeCode Available	1
Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities	Jun 1, 2021	Graph EmbeddingKnowledge Graph Embedding	CodeCode Available	0
Mixup for Node and Graph Classification	Jun 1, 2021	ClassificationData Augmentation	CodeCode Available	1
Relational Graph Neural Network Design via Progressive Neural Architecture Search	May 30, 2021	Graph Neural NetworkNeural Architecture Search	—Unverified	0
_2-norm Flow Diffusion in Near-Linear Time	May 30, 2021	ClusteringGraph Clustering	—Unverified	0
GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily	May 28, 2021	ClusteringNode Classification	—Unverified	0
Local, global and scale-dependent node roles	May 26, 2021	Graph LearningGraph Neural Network	CodeCode Available	0
Self-Supervised Graph Representation Learning via Topology Transformations	May 25, 2021	Graph ClassificationGraph Representation Learning	CodeCode Available	0
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification	May 25, 2021	Ensemble LearningGraph Neural Network	—Unverified	0
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions	May 24, 2021	Link PredictionNode Classification	CodeCode Available	0
Heterogeneous Graph Representation Learning with Relation Awareness	May 24, 2021	Graph LearningGraph Neural Network	CodeCode Available	1
A Robust and Generalized Framework for Adversarial Graph Embedding	May 22, 2021	Generative Adversarial NetworkGraph Embedding	CodeCode Available	0
Auxiliary learning induced graph convolutional networks	May 21, 2021	Auxiliary LearningClassification	—Unverified	0
Ultrahyperbolic Neural Networks	May 21, 2021	Node Classification	—Unverified	0
Is Heterophily A Real Nightmare For Graph Neural Networks Performing Node Classification?	May 21, 2021	Inductive BiasNode Classification	—Unverified	0
Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling	May 19, 2021	ClusteringGPU	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	TransGNN	1:1 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	GEM	Accuracy	74.2	—	Unverified
6	GGCM	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	Truncated Krylov	Accuracy	81.7	—	Unverified
5	SuperGAT MX	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