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

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Showing 1526–1550 of 1860 papers

Title	Date	Tasks	Status
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks	Sep 30, 2020	Graph ClassificationLink Prediction	—Unverified
Spectral Embedding of Graph Networks	Sep 30, 2020	Graph EmbeddingNode Classification	—Unverified
Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information	Sep 29, 2020	General ClassificationNode Classification	CodeCode Available
Framework for Designing Filters of Spectral Graph Convolutional Neural Networks in the Context of Regularization Theory	Sep 29, 2020	Graph LearningNode Classification	CodeCode Available
Revisiting Graph Neural Networks for Link Prediction	Sep 28, 2020	ClassificationGraph Classification	—Unverified
PanRep: Universal node embeddings for heterogeneous graphs	Sep 28, 2020	Graph Neural NetworkLink Prediction	—Unverified
Graph Joint Attention Networks	Sep 28, 2020	BenchmarkingGraph Attention	—Unverified
Inductive Graph Embeddings through Locality Encodings	Sep 26, 2020	Language ModelingLanguage Modelling	—Unverified
Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective	Sep 24, 2020	General ClassificationNode Classification	—Unverified
Streaming Graph Neural Networks via Continual Learning	Sep 23, 2020	Continual LearningNode Classification	—Unverified
Layer-stacked Attention for Heterogeneous Network Embedding	Sep 17, 2020	Graph Neural NetworkNetwork Embedding	—Unverified
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation	Sep 11, 2020	Inductive LearningNode Classification	CodeCode Available
Community detection in networks using graph embeddings	Sep 11, 2020	ClusteringCommunity Detection	—Unverified
CatGCN: Graph Convolutional Networks with Categorical Node Features	Sep 11, 2020	Graph LearningNode Classification	CodeCode Available
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs	Sep 9, 2020	Knowledge GraphsLanguage Modelling	CodeCode Available
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing	Sep 5, 2020	Graph MiningGraph Reconstruction	CodeCode Available
LFGCN: Levitating over Graphs with Levy Flights	Sep 4, 2020	Node Classification	—Unverified
SAIL: Self-Augmented Graph Contrastive Learning	Sep 2, 2020	Contrastive LearningKnowledge Distillation	—Unverified
Beyond Observed Connections : Link Injection	Sep 2, 2020	Link PredictionNode Classification	CodeCode Available
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function	Sep 1, 2020	Graph Neural NetworkNode Classification	CodeCode Available
Decoupled Variational Embedding for Signed Directed Networks	Aug 28, 2020	Link Sign PredictionNode Classification	CodeCode Available
DVE: Dynamic Variational Embeddings with Applications in Recommender Systems	Aug 27, 2020	Link PredictionNode Classification	—Unverified
Learning Node Representations against Perturbations	Aug 26, 2020	Contrastive LearningInductive Learning	CodeCode Available
Tackling Over-Smoothing for General Graph Convolutional Networks	Aug 22, 2020	Node Classification	—Unverified
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks	Aug 20, 2020	Graph ClassificationNode Classification	—Unverified

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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