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 1551–1575 of 1860 papers

Title	Date	Tasks	Status
GraphReach: Position-Aware Graph Neural Network using Reachability Estimations	Aug 19, 2020	Graph Neural NetworkLink Prediction	CodeCode Available
Degree-Quant: Quantization-Aware Training for Graph Neural Networks	Aug 11, 2020	CPUGraph Classification	—Unverified
DINE: A Framework for Deep Incomplete Network Embedding	Aug 9, 2020	General ClassificationLink Prediction	—Unverified
Multivariate Relations Aggregation Learning in Social Networks	Aug 9, 2020	AttributeGraph Learning	—Unverified
Directed hypergraph neural network	Aug 9, 2020	General ClassificationNode Classification	—Unverified
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs	Jul 31, 2020	ClusteringGraph Neural Network	—Unverified
node2coords: Graph Representation Learning with Wasserstein Barycenters	Jul 31, 2020	DecoderGraph Representation Learning	—Unverified
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification	Jul 27, 2020	ClassificationGeneral Classification	—Unverified
Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective	Jul 17, 2020	Community DetectionDistributed Computing	—Unverified
GraphCL: Contrastive Self-Supervised Learning of Graph Representations	Jul 15, 2020	Contrastive LearningInductive Learning	—Unverified
Are Hyperbolic Representations in Graphs Created Equal?	Jul 15, 2020	Graph LearningGraph Representation Learning	—Unverified
Next Waves in Veridical Network Embedding	Jul 10, 2020	Community DetectionLink Prediction	—Unverified
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation	Jul 9, 2020	Active LearningClassification	—Unverified
Pre-Trained Models for Heterogeneous Information Networks	Jul 7, 2020	ClusteringLink Prediction	—Unverified
Faster Graph Embeddings via Coarsening	Jul 6, 2020	Link PredictionNode Classification	CodeCode Available
AEGCN: An Autoencoder-Constrained Graph Convolutional Network	Jul 3, 2020	DecoderGraph Attention	CodeCode Available
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach	Jul 2, 2020	Graph Representation LearningNode Classification	CodeCode Available
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation	Jul 1, 2020	Node Classification	—Unverified
Graph Clustering with Graph Neural Networks	Jun 30, 2020	AttributeClustering	—Unverified
Policy-GNN: Aggregation Optimization for Graph Neural Networks	Jun 26, 2020	Deep Reinforcement LearningNode Classification	CodeCode Available
Non-Parametric Graph Learning for Bayesian Graph Neural Networks	Jun 23, 2020	Graph LearningLink Prediction	—Unverified
Connecting Graph Convolutional Networks and Graph-Regularized PCA	Jun 22, 2020	Node Classification	—Unverified
Quantifying Challenges in the Application of Graph Representation Learning	Jun 18, 2020	Graph Representation LearningLink Prediction	—Unverified
NodeNet: A Graph Regularised Neural Network for Node Classification	Jun 16, 2020	ClassificationGeneral Classification	—Unverified
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification	Jun 15, 2020	Graph AttentionNode Classification	—Unverified

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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