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

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Showing 1251–1275 of 1860 papers

Title	Date	Tasks	Status
Simplified Graph Convolution with Heterophily	Feb 8, 2022	Node Classification	CodeCode Available
Bandit Sampling for Multiplex Networks	Feb 8, 2022	Link PredictionNode Classification	—Unverified
FMP: Toward Fair Graph Message Passing against Topology Bias	Feb 8, 2022	Contrastive LearningFairness	—Unverified
Neural Models for Output-Space Invariance in Combinatorial Problems	Feb 7, 2022	Node Classification	—Unverified
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification	Feb 5, 2022	ClassificationGraph Classification	—Unverified
Dimensionality Reduction Meets Message Passing for Graph Node Embeddings	Feb 1, 2022	Dimensionality ReductionNode Classification	CodeCode Available
Investigating Transfer Learning in Graph Neural Networks	Feb 1, 2022	ClassificationGraph Classification	—Unverified
Learning Robust Representation through Graph Adversarial Contrastive Learning	Jan 31, 2022	Contrastive LearningGraph Representation Learning	—Unverified
SMGRL: Scalable Multi-resolution Graph Representation Learning	Jan 29, 2022	Graph Representation LearningNode Classification	CodeCode Available
Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification	Jan 27, 2022	ClassificationGraph Attention	—Unverified
Partition-Based Active Learning for Graph Neural Networks	Jan 23, 2022	Active LearningNode Classification	CodeCode Available
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach	Jan 22, 2022	Graph Representation LearningLink Prediction	—Unverified
Fair Node Representation Learning via Adaptive Data Augmentation	Jan 21, 2022	Contrastive LearningData Augmentation	—Unverified
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning	Jan 21, 2022	Contrastive LearningGraph Representation Learning	—Unverified
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels	Jan 20, 2022	InformativenessNode Classification	—Unverified
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning	Jan 16, 2022	Few-Shot LearningLink Prediction	CodeCode Available
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution	Jan 11, 2022	Graph LearningGraph Neural Network	CodeCode Available
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks	Jan 8, 2022	Link PredictionNetwork Embedding	—Unverified
Neighbor2vec: an efficient and effective method for Graph Embedding	Jan 7, 2022	Graph EmbeddingLink Prediction	—Unverified
Asymptotics of _2 Regularized Network Embeddings	Jan 5, 2022	Link PredictionNode Classification	CodeCode Available
Graph Decipher: A transparent dual-attention graph neural network to understand the message-passing mechanism for the node classification	Jan 4, 2022	Graph Neural NetworkNode Classification	—Unverified
Toward the Analysis of Graph Neural Networks	Jan 1, 2022	Node Classification	—Unverified
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning	Dec 22, 2021	Link PredictionLink Property Prediction	CodeCode Available
SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks	Dec 22, 2021	Link PredictionNode Classification	CodeCode Available
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods	Dec 20, 2021	ClusteringCPU	—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	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	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