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 1426–1450 of 1860 papers

Title	Date	Tasks	Status	Hype
Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information	Sep 29, 2020	General ClassificationNode Classification	CodeCode Available	0
Revisiting Graph Neural Networks for Link Prediction	Sep 28, 2020	ClassificationGraph Classification	—Unverified	0
Graph Joint Attention Networks	Sep 28, 2020	BenchmarkingGraph Attention	—Unverified	0
PanRep: Universal node embeddings for heterogeneous graphs	Sep 28, 2020	Graph Neural NetworkLink Prediction	—Unverified	0
Inductive Graph Embeddings through Locality Encodings	Sep 26, 2020	Language ModelingLanguage Modelling	—Unverified	0
Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective	Sep 24, 2020	General ClassificationNode Classification	—Unverified	0
Learning Graph Normalization for Graph Neural Networks	Sep 24, 2020	Graph ClassificationGraph Regression	CodeCode Available	1
Streaming Graph Neural Networks via Continual Learning	Sep 23, 2020	Continual LearningNode Classification	—Unverified	0
Force2Vec: Parallel force-directed graph embedding	Sep 17, 2020	ClusteringGraph Embedding	CodeCode Available	1
Layer-stacked Attention for Heterogeneous Network Embedding	Sep 17, 2020	Graph Neural NetworkNetwork Embedding	—Unverified	0
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning	Sep 15, 2020	ClusteringGraph Representation Learning	CodeCode Available	1
CatGCN: Graph Convolutional Networks with Categorical Node Features	Sep 11, 2020	Graph LearningNode Classification	CodeCode Available	0
Community detection in networks using graph embeddings	Sep 11, 2020	ClusteringCommunity Detection	—Unverified	0
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation	Sep 11, 2020	Inductive LearningNode Classification	CodeCode Available	0
Understanding Coarsening for Embedding Large-Scale Graphs	Sep 10, 2020	Anomaly DetectionGraph Embedding	CodeCode Available	1
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs	Sep 9, 2020	Knowledge GraphsLanguage Modelling	CodeCode Available	0
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations	Sep 8, 2020	Node ClassificationStructural Node Embedding	CodeCode Available	1
Hierarchical Message-Passing Graph Neural Networks	Sep 8, 2020	Community DetectionGraph Neural Network	CodeCode Available	1
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification	Sep 8, 2020	General ClassificationGraph Neural Network	CodeCode Available	1
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing	Sep 5, 2020	Graph MiningGraph Reconstruction	CodeCode Available	0
LFGCN: Levitating over Graphs with Levy Flights	Sep 4, 2020	Node Classification	—Unverified	0
Rethinking Graph Regularization for Graph Neural Networks	Sep 4, 2020	Node ClassificationRepresentation Learning	CodeCode Available	1
Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information	Sep 3, 2020	AttributeFairness	CodeCode Available	1
Beyond Observed Connections : Link Injection	Sep 2, 2020	Link PredictionNode Classification	CodeCode Available	0
Mutual Teaching for Graph Convolutional Networks	Sep 2, 2020	Node ClassificationPseudo Label	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