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 1026–1050 of 1860 papers

Title	Date	Tasks	Status	Hype
MarkovGNN: Graph Neural Networks on Markov Diffusion	Feb 5, 2022	ClusteringNode Classification	CodeCode Available	1
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited	Feb 4, 2022	GPRGraph Learning	CodeCode Available	1
Graph Coloring with Physics-Inspired Graph Neural Networks	Feb 3, 2022	ClusteringCommunity Detection	CodeCode Available	1
Investigating Transfer Learning in Graph Neural Networks	Feb 1, 2022	ClassificationGraph Classification	—Unverified	0
When Do Flat Minima Optimizers Work?	Feb 1, 2022	BenchmarkingGraph Learning	CodeCode Available	1
Dimensionality Reduction Meets Message Passing for Graph Node Embeddings	Feb 1, 2022	Dimensionality ReductionNode Classification	CodeCode Available	0
Learning Robust Representation through Graph Adversarial Contrastive Learning	Jan 31, 2022	Contrastive LearningGraph Representation Learning	—Unverified	0
Graph Representation Learning via Aggregation Enhancement	Jan 30, 2022	Data AugmentationGraph Representation Learning	CodeCode Available	1
SMGRL: Scalable Multi-resolution Graph Representation Learning	Jan 29, 2022	Graph Representation LearningNode Classification	CodeCode Available	0
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks	Jan 28, 2022	Federated LearningNode Classification	CodeCode Available	1
Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification	Jan 27, 2022	ClassificationGraph Attention	—Unverified	0
Partition-Based Active Learning for Graph Neural Networks	Jan 23, 2022	Active LearningNode Classification	CodeCode Available	0
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach	Jan 22, 2022	Graph Representation LearningLink Prediction	—Unverified	0
Fair Node Representation Learning via Adaptive Data Augmentation	Jan 21, 2022	Contrastive LearningData Augmentation	—Unverified	0
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning	Jan 21, 2022	Contrastive LearningGraph Representation Learning	—Unverified	0
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation	Jan 20, 2022	Adversarial AttackContrastive Learning	CodeCode Available	1
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels	Jan 20, 2022	InformativenessNode Classification	—Unverified	0
Decoupling the Depth and Scope of Graph Neural Networks	Jan 19, 2022	Link PredictionNode Classification	CodeCode Available	1
Towards Unsupervised Deep Graph Structure Learning	Jan 17, 2022	Contrastive LearningGraph structure learning	CodeCode Available	1
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	0
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution	Jan 11, 2022	Graph LearningGraph Neural Network	CodeCode Available	0
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks	Jan 8, 2022	Link PredictionNetwork Embedding	—Unverified	0
Neighbor2vec: an efficient and effective method for Graph Embedding	Jan 7, 2022	Graph EmbeddingLink Prediction	—Unverified	0
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs	Jan 7, 2022	DecoderGraph Neural Network	CodeCode Available	1
Towards Similarity-Aware Time-Series Classification	Jan 5, 2022	ClassificationDynamic Time Warping	CodeCode Available	1

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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