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 1301–1325 of 1860 papers

Title	Date	Tasks	Status	Hype
Generalized Equivariance and Preferential Labeling for GNN Node Classification	Feb 23, 2021	ClassificationGeneral Classification	CodeCode Available	0
MagNet: A Neural Network for Directed Graphs	Feb 22, 2021	Link PredictionNode Classification	CodeCode Available	1
A Deep Graph Wavelet Convolutional Neural Network for Semi-supervised Node Classification	Feb 19, 2021	General ClassificationNode Classification	CodeCode Available	1
Fast Graph Learning with Unique Optimal Solutions	Feb 17, 2021	FormGraph Learning	CodeCode Available	1
NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique	Feb 17, 2021	Graph Neural NetworkNode Classification	CodeCode Available	1
Evaluating Node Embeddings of Complex Networks	Feb 16, 2021	Community DetectionGraph Embedding	CodeCode Available	1
Topological Graph Neural Networks	Feb 15, 2021	Graph LearningNode Classification	CodeCode Available	1
HDMI: High-order Deep Multiplex Infomax	Feb 15, 2021	Node ClassificationRepresentation Learning	CodeCode Available	1
Adversarial Attack on Network Embeddings via Supervised Network Poisoning	Feb 14, 2021	Adversarial AttackLink Prediction	CodeCode Available	0
A Statistical Relational Approach to Learning Distance-based GCNs	Feb 13, 2021	Density EstimationLink Prediction	—Unverified	0
A Unified Lottery Ticket Hypothesis for Graph Neural Networks	Feb 12, 2021	Link PredictionNode Classification	CodeCode Available	1
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks	Feb 12, 2021	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	CodeCode Available	1
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks	Feb 12, 2021	Link PredictionNetwork Embedding	CodeCode Available	1
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information Networks	Feb 11, 2021	Anomaly DetectionFraud Detection	CodeCode Available	0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes	Feb 6, 2021	AttributeGraph Neural Network	—Unverified	0
Cleora: A Simple, Strong and Scalable Graph Embedding Scheme	Feb 3, 2021	Contrastive LearningCPU	CodeCode Available	1
Learning Graph Representations	Feb 3, 2021	General ClassificationGraph Classification	—Unverified	0
Infant Cry Classification with Graph Convolutional Networks	Jan 31, 2021	ClassificationDiversity	—Unverified	0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection	Jan 27, 2021	Active LearningArticles	—Unverified	0
Calibrating and Improving Graph Contrastive Learning	Jan 27, 2021	Contrastive LearningGraph Clustering	CodeCode Available	0
Efficient Graph Deep Learning in TensorFlow with tf_geometric	Jan 27, 2021	Deep LearningGeneral Classification	CodeCode Available	1
Generating a Doppelganger Graph: Resembling but Distinct	Jan 23, 2021	BenchmarkingGraph Representation Learning	CodeCode Available	1
NEMR: Network Embedding on Metric of Relation	Jan 20, 2021	Link PredictionNetwork Embedding	—Unverified	0
Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks	Jan 19, 2021	Node Classification	—Unverified	0
Edge-Featured Graph Attention Network	Jan 19, 2021	Graph AttentionGraph Learning	—Unverified	0

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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