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 376–400 of 1860 papers

Title	Date	Tasks	Status	Hype	Score
Deep Learning for Abstract Argumentation Semantics	Jul 15, 2020	Abstract ArgumentationDeep Learning	CodeCode Available	1	5
From Hypergraph Energy Functions to Hypergraph Neural Networks	Jun 16, 2023	Bilevel OptimizationGraph Neural Network	CodeCode Available	1	5
Node Attribute Generation on Graphs	Jul 23, 2019	AttributeData Augmentation	CodeCode Available	1	5
Fuzzy Graph Neural Network for Few-Shot Learning	Jul 19, 2020	Few-Shot LearningGraph Neural Network	CodeCode Available	1	5
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks	Jun 20, 2022	Node Classification	CodeCode Available	1	5
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs	Jun 23, 2021	Knowledge GraphsLink Prediction	CodeCode Available	1	5
Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization	Mar 23, 2022	Node ClassificationRepresentation Learning	CodeCode Available	1	5
Deformable Graph Convolutional Networks	Dec 29, 2021	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	CodeCode Available	1	5
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability	Jun 13, 2024	Graph ClassificationGraph Regression	CodeCode Available	1	5
Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching	Dec 16, 2021	Node ClassificationRepresentation Learning	CodeCode Available	1	5
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training	Jun 17, 2020	Contrastive LearningGeneral Classification	CodeCode Available	1	5
Graph Geometry Interaction Learning	Oct 23, 2020	Link PredictionNode Classification	CodeCode Available	1	5
On the Connection Between MPNN and Graph Transformer	Jan 27, 2023	Graph ClassificationGraph Learning	CodeCode Available	1	5
Bayesian Attention Modules	Oct 20, 2020	Image CaptioningMachine Translation	CodeCode Available	1	5
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features	Nov 23, 2021	GPUNode Classification	CodeCode Available	1	5
A Meta-Learning Approach for Training Explainable Graph Neural Networks	Sep 20, 2021	Graph ClassificationMeta-Learning	CodeCode Available	1	5
Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation	May 27, 2022	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Generative Subgraph Contrast for Self-Supervised Graph Representation Learning	Jul 25, 2022	Contrastive LearningGraph Representation Learning	CodeCode Available	1	5
Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing	Feb 3, 2023	Node Classification	CodeCode Available	1	5
Bayesian Graph Neural Networks with Adaptive Connection Sampling	Jun 7, 2020	Node Classification	CodeCode Available	1	5
PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs	Jul 20, 2020	Graph Neural NetworkLink Prediction	CodeCode Available	1	5
PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering	Dec 22, 2023	Graph Representation LearningNode Classification	CodeCode Available	1	5
Perception-Inspired Graph Convolution for Music Understanding Tasks	May 15, 2024	Graph ClassificationGraph Learning	CodeCode Available	1	5
A Deep Graph Wavelet Convolutional Neural Network for Semi-supervised Node Classification	Feb 19, 2021	General ClassificationNode Classification	CodeCode Available	1	5
DiffWire: Inductive Graph Rewiring via the Lovász Bound	Jun 15, 2022	Graph ClassificationNode Classification	CodeCode Available	1	5

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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