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 176–200 of 1860 papers

Title	Date	Tasks	Status	Hype
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020	Nov 25, 2021	Graph ClassificationNode Classification	CodeCode Available	1
Gated Graph Sequence Neural Networks	Nov 17, 2015	Drug DiscoveryGraph Classification	CodeCode Available	1
Diffusion Improves Graph Learning	Oct 28, 2019	ClusteringGraph Learning	CodeCode Available	1
A data-centric approach for assessing progress of Graph Neural Networks	Jun 18, 2024	ClassificationMulti-class Classification	CodeCode Available	1
Automated Self-Supervised Learning for Graphs	Jun 10, 2021	ClusteringNode Classification	CodeCode Available	1
Automatic Relation-aware Graph Network Proliferation	May 31, 2022	Graph ClassificationGraph Learning	CodeCode Available	1
AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning	Jul 26, 2024	Graph ClassificationKnowledge Graphs	CodeCode Available	1
Can GNN be Good Adapter for LLMs?	Feb 20, 2024	Graph Neural NetworkNode Classification	CodeCode Available	1
Geom-GCN: Geometric Graph Convolutional Networks	Feb 13, 2020	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	CodeCode Available	1
Backdoor Attacks to Graph Neural Networks	Jun 19, 2020	Backdoor AttackGeneral Classification	CodeCode Available	1
Bag of Tricks for Node Classification with Graph Neural Networks	Mar 24, 2021	ClassificationGeneral Classification	CodeCode Available	1
Adversarial Training Methods for Network Embedding	Aug 30, 2019	Link PredictionNetwork Embedding	CodeCode Available	1
GRAF: Graph Attention-aware Fusion Networks	Mar 29, 2023	Graph AttentionGraph Neural Network	CodeCode Available	1
GRAND+: Scalable Graph Random Neural Networks	Mar 12, 2022	Data AugmentationGraph Learning	CodeCode Available	1
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding	Oct 12, 2021	Edge ClassificationGraph Representation Learning	CodeCode Available	1
Deep Graph Contrastive Representation Learning	Jun 7, 2020	AttributeContrastive Learning	CodeCode Available	1
CAT-Walk: Inductive Hypergraph Learning via Set Walks	Jun 19, 2023	Hyperedge PredictionNode Classification	CodeCode Available	1
Graph Coloring with Physics-Inspired Graph Neural Networks	Feb 3, 2022	ClusteringCommunity Detection	CodeCode Available	1
Bayesian Attention Modules	Oct 20, 2020	Image CaptioningMachine Translation	CodeCode Available	1
A Meta-Learning Approach for Training Explainable Graph Neural Networks	Sep 20, 2021	Graph ClassificationMeta-Learning	CodeCode Available	1
Graph Contrastive Learning with Adaptive Augmentation	Oct 27, 2020	AttributeContrastive Learning	CodeCode Available	1
Bayesian Graph Neural Networks with Adaptive Connection Sampling	Jun 7, 2020	Node Classification	CodeCode Available	1
Graph Convolutional Networks for Road Networks	Aug 30, 2019	AttributeBIG-bench Machine Learning	CodeCode Available	1
A Deep Graph Wavelet Convolutional Neural Network for Semi-supervised Node Classification	Feb 19, 2021	General ClassificationNode Classification	CodeCode Available	1
Beyond Low-frequency Information in Graph Convolutional Networks	Jan 4, 2021	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	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	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