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 1726–1750 of 1860 papers

Title	Date	Tasks	Status
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification	Jun 5, 2019	General ClassificationGraph Classification	CodeCode Available
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples	Jun 3, 2019	ClusteringNode Classification	—Unverified
Exploiting Edge Features for Graph Neural Networks	Jun 1, 2019	General ClassificationGraph Attention	—Unverified
Graph Learning Network: A Structure Learning Algorithm	May 29, 2019	Community DetectionGeneral Classification	CodeCode Available
Triple2Vec: Learning Triple Embeddings from Knowledge Graphs	May 28, 2019	ClusteringGraph Embedding	—Unverified
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification	May 27, 2019	ClassificationGeneral Classification	CodeCode Available
Graph Attention Auto-Encoders	May 26, 2019	DecoderGraph Attention	CodeCode Available
Meta-GNN: On Few-shot Node Classification in Graph Meta-learning	May 23, 2019	Few-Shot LearningGeneral Classification	CodeCode Available
Relation Structure-Aware Heterogeneous Information Network Embedding	May 15, 2019	ClusteringLink Prediction	—Unverified
Graph U-Nets	May 11, 2019	DecoderGeneral Classification	CodeCode Available
Network Representation Learning: Consolidation and Renewed Bearing	May 2, 2019	Dimensionality ReductionGeneral Classification	CodeCode Available
Few-shot Classification on Graphs with Structural Regularized GCNs	May 1, 2019	ClassificationFew-Shot Learning	—Unverified
CGNF: Conditional Graph Neural Fields	May 1, 2019	General ClassificationNode Classification	—Unverified
Graph Convolutional Networks with EigenPooling	Apr 30, 2019	ClassificationGeneral Classification	CodeCode Available
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing	Apr 30, 2019	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	CodeCode Available
Advancing GraphSAGE with A Data-Driven Node Sampling	Apr 29, 2019	General ClassificationGraph Neural Network	CodeCode Available
Robust Graph Data Learning via Latent Graph Convolutional Representation	Apr 26, 2019	Graph LearningNode Classification	—Unverified
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification	Apr 23, 2019	General ClassificationGraph Learning	—Unverified
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning	Apr 22, 2019	General ClassificationInductive Learning	CodeCode Available
Compositional Network Embedding	Apr 17, 2019	AttributeLink Prediction	—Unverified
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective	Apr 10, 2019	ClassificationGeneral Classification	CodeCode Available
Topological based classification of paper domains using graph convolutional networks	Apr 10, 2019	ClassificationGeneral Classification	—Unverified
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks	Apr 9, 2019	General ClassificationNode Classification	—Unverified
DeepGCNs: Can GCNs Go as Deep as CNNs?	Apr 7, 2019	3D Semantic SegmentationGraph Classification	CodeCode Available
Quantum-based subgraph convolutional neural networks	Apr 1, 2019	General ClassificationGraph 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	NodeNet	Accuracy	86.8	—	Unverified
7	Cleora	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	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