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 1701–1725 of 1860 papers

Title	Date	Tasks	Status
Deep Hashing for Signed Social Network Embedding	Aug 12, 2019	Deep HashingLink Prediction	—Unverified
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks	Aug 10, 2019	Graph ClassificationGraph Embedding	—Unverified
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction	Aug 7, 2019	Graph AttentionGraph Classification	CodeCode Available
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings	Aug 6, 2019	ClassificationDocument Classification	—Unverified
Semi-Supervised Tensor Factorization for Node Classification in Complex Social Networks	Jul 24, 2019	General ClassificationNode Classification	—Unverified
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling	Jul 20, 2019	Graph EmbeddingNetwork Embedding	—Unverified
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics	Jul 19, 2019	Clinical KnowledgeLink Prediction	CodeCode Available
k-hop Graph Neural Networks	Jul 13, 2019	General ClassificationGraph Classification	CodeCode Available
Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification	Jul 12, 2019	ClassificationGeneral Classification	—Unverified
Fast Haar Transforms for Graph Neural Networks	Jul 10, 2019	General ClassificationNode Classification	—Unverified
Label-Aware Graph Convolutional Networks	Jul 10, 2019	ClassificationGeneral Classification	—Unverified
Dimensional Reweighting Graph Convolutional Networks	Jul 4, 2019	Node Classification	CodeCode Available
Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation	Jul 4, 2019	Graph ClassificationGraph Representation Learning	CodeCode Available
Structure fusion based on graph convolutional networks for semi-supervised classification	Jul 2, 2019	ClassificationGeneral Classification	—Unverified
Certifiable Robustness and Robust Training for Graph Convolutional Networks	Jun 28, 2019	Node Classification	CodeCode Available
Graph Star Net for Generalized Multi-Task Learning	Jun 21, 2019	ClassificationGeneral Classification	CodeCode Available
ANAE: Learning Node Context Representation for Attributed Network Embedding	Jun 20, 2019	AttributeDecoder	—Unverified
Regional based query in graph active learning	Jun 20, 2019	Active LearningClassification	CodeCode Available
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank	Jun 17, 2019	CPUGraph Reconstruction	—Unverified
Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach	Jun 13, 2019	Graph EmbeddingGraph Mining	—Unverified
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations	Jun 12, 2019	Graph EmbeddingLink Prediction	CodeCode Available
Heterogeneous network approach to predict individuals' mental health	Jun 11, 2019	Node ClassificationRecommendation Systems	—Unverified
Attacking Graph Convolutional Networks via Rewiring	Jun 10, 2019	General ClassificationGraph Classification	—Unverified
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective	Jun 10, 2019	Adversarial RobustnessClassification	CodeCode Available
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	Jun 5, 2019	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	CodeCode Available

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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	GEM	Accuracy	74.2	—	Unverified
6	GGCM	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