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 1651–1675 of 1860 papers

Title	Date	Tasks	Status
Semi-supervisedly Co-embedding Attributed Networks	Oct 31, 2019	AttributeNode Classification	CodeCode Available
Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled Data	Oct 28, 2019	ClassificationFew-Shot Learning	CodeCode Available
Pre-train and Learn: Preserve Global Information for Graph Neural Networks	Oct 27, 2019	AttributeNode Classification	CodeCode Available
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning	Oct 26, 2019	Bayesian InferenceGeneral Classification	—Unverified
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters	Oct 24, 2019	Document ClassificationGeneral Classification	CodeCode Available
Network2Vec Learning Node Representation Based on Space Mapping in Networks	Oct 23, 2019	General ClassificationLink Prediction	—Unverified
Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification	Oct 22, 2019	ClassificationGeneral Classification	CodeCode Available
Recurrent Attention Walk for Semi-supervised Classification	Oct 22, 2019	ClassificationGeneral Classification	CodeCode Available
Active Learning for Graph Neural Networks via Node Feature Propagation	Oct 16, 2019	Active LearningClustering	—Unverified
Link Prediction via Graph Attention Network	Oct 10, 2019	Graph AttentionInformation Retrieval	—Unverified
Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs	Oct 7, 2019	hyperedge classificationhypergraph embedding	CodeCode Available
Graph Few-shot Learning via Knowledge Transfer	Oct 7, 2019	Few-Shot LearningGeneral Classification	CodeCode Available
Effective Stabilized Self-Training on Few-Labeled Graph Data	Oct 7, 2019	BenchmarkingModel Selection	CodeCode Available
Rethinking Kernel Methods for Node Representation Learning on Graphs	Oct 6, 2019	ClassificationGeneral Classification	CodeCode Available
Dynamic Embedding on Textual Networks via a Gaussian Process	Oct 5, 2019	Link PredictionNetwork Embedding	CodeCode Available
Dynamic Joint Variational Graph Autoencoders	Oct 4, 2019	ClusteringGraph Clustering	—Unverified
Learning Robust Representations with Graph Denoising Policy Network	Oct 4, 2019	DenoisingGraph Representation Learning	—Unverified
On the Equivalence between Positional Node Embeddings and Structural Graph Representations	Oct 1, 2019	Inductive LearningLink Prediction	CodeCode Available
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs	Sep 26, 2019	AttributeClustering	CodeCode Available
Dimensional Reweighting Graph Convolution Networks	Sep 25, 2019	Node Classification	—Unverified
Active Learning Graph Neural Networks via Node Feature Propagation	Sep 25, 2019	Active LearningNode Classification	—Unverified
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions	Sep 25, 2019	Community DetectionGraph Embedding	—Unverified
GraphMix: Improved Training of GNNs for Semi-Supervised Learning	Sep 25, 2019	Generalization BoundsGraph Attention	CodeCode Available
Ordinary differential equations on graph networks	Sep 25, 2019	Graph ClassificationGraph Neural Network	—Unverified
Octave Graph Convolutional Network	Sep 25, 2019	Node ClassificationRepresentation Learning	—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	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