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 976–1000 of 1860 papers

Title	Date	Tasks	Status	Hype
RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning	Apr 29, 2022	Contrastive LearningDiversity	CodeCode Available	1
Detecting Topology Attacks against Graph Neural Networks	Apr 21, 2022	Node Classification	—Unverified	0
Effects of Graph Convolutions in Multi-layer Networks	Apr 20, 2022	Node ClassificationStochastic Block Model	—Unverified	0
Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph Convolutional Network	Apr 20, 2022	Graph Neural NetworkNode Classification	—Unverified	0
Label Efficient Regularization and Propagation for Graph Node Classification	Apr 19, 2022	AttributeBenchmarking	—Unverified	0
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction	Apr 18, 2022	AttributeGraph Classification	CodeCode Available	0
Neural Structured Prediction for Inductive Node Classification	Apr 15, 2022	ClassificationNode Classification	CodeCode Available	1
ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification	Apr 15, 2022	Event Causality IdentificationNode Classification	—Unverified	0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations	Apr 12, 2022	Community DetectionGraph Representation Learning	CodeCode Available	0
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs	Apr 11, 2022	Node Classification	CodeCode Available	0
Multi-view graph structure learning using subspace merging on Grassmann manifold	Apr 11, 2022	Graph ClassificationGraph structure learning	—Unverified	0
Graph Ordering Attention Networks	Apr 11, 2022	Node Classification	CodeCode Available	1
Explicit Feature Interaction-aware Graph Neural Networks	Apr 7, 2022	Graph Neural NetworkNode Classification	CodeCode Available	0
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020	Apr 6, 2022	AutoMLGPU	CodeCode Available	0
A Survey on Graph Representation Learning Methods	Apr 4, 2022	Anomaly DetectionGraph Embedding	—Unverified	0
GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment	Apr 4, 2022	Node ClassificationSelf-Supervised Learning	CodeCode Available	1
Synthetic Graph Generation to Benchmark Graph Learning	Apr 4, 2022	Graph GenerationGraph Learning	—Unverified	0
Message Passing Neural Networks for Hypergraphs	Mar 31, 2022	Graph Neural NetworkHypergraph representations	—Unverified	0
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation	Mar 30, 2022	ClassificationNode Classification	—Unverified	0
Supervised Graph Contrastive Learning for Few-shot Node Classification	Mar 29, 2022	ClassificationContrastive Learning	—Unverified	0
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs	Mar 28, 2022	CPUGPU	CodeCode Available	2
Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization	Mar 23, 2022	Node ClassificationRepresentation Learning	CodeCode Available	1
A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks	Mar 23, 2022	ClassificationHierarchical Multi-label Classification	—Unverified	0
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling	Mar 21, 2022	Node Classification	CodeCode Available	1
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily	Mar 19, 2022	Node Classification	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	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