Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 401–425 of 982 papers

Title	Date	Tasks	Status	Score
Hyper-SAGNN: a self-attention based graph neural network for hypergraphs	Nov 6, 2019	Graph Neural NetworkGraph Representation Learning	CodeCode Available	5
Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community Labeling	Jan 20, 2022	Community DetectionGraph Embedding	CodeCode Available	5
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks	May 8, 2024	Graph ClassificationGraph Learning	CodeCode Available	5
Hyperbolic Neural Networks	May 23, 2018	Graph Representation LearningNatural Language Inference	CodeCode Available	5
Robust Graph Representation Learning for Local Corruption Recovery	Feb 10, 2022	Graph EmbeddingGraph Learning	CodeCode Available	5
Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition	Jun 8, 2023	Event data classificationGraph Representation Learning	CodeCode Available	5
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation Learning	Oct 18, 2023	Graph EmbeddingGraph Representation Learning	CodeCode Available	5
Hyperparameter-free and Explainable Whole Graph Embedding	Aug 4, 2021	Graph ClassificationGraph Embedding	CodeCode Available	5
HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations	Aug 12, 2021	Graph EmbeddingGraph Representation Learning	CodeCode Available	5
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model	Jan 23, 2023	Graph Representation LearningRepresentation Learning	CodeCode Available	5
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification	Apr 11, 2023	Graph Representation LearningNode Classification	CodeCode Available	5
LASE: Learned Adjacency Spectral Embeddings	Dec 23, 2024	Graph AttentionGraph Neural Network	CodeCode Available	5
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement	Jun 3, 2024	Anomaly DetectionDecision Making	CodeCode Available	5
Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks	Oct 16, 2023	Contrastive LearningGraph Representation Learning	CodeCode Available	5
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning	Jul 3, 2023	Contrastive LearningData Augmentation	CodeCode Available	5
Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening	Jul 7, 2020	Graph Representation LearningRepresentation Learning	CodeCode Available	5
Graph Pooling via Coarsened Graph Infomax	May 4, 2021	Contrastive LearningGraph Representation Learning	CodeCode Available	5
Centrality Graph Shift Operators for Graph Neural Networks	Nov 7, 2024	Graph AttentionGraph Neural Network	CodeCode Available	5
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding	Jan 5, 2021	Graph Representation LearningRepresentation Learning	CodeCode Available	5
ARIEL: Adversarial Graph Contrastive Learning	Aug 15, 2022	Contrastive LearningData Augmentation	CodeCode Available	5
Graph Representation Learning: A Survey	Sep 3, 2019	FormGraph Embedding	CodeCode Available	5
Graph Representation Learning Beyond Node and Homophily	Mar 3, 2022	Edge ClassificationGraph Embedding	CodeCode Available	5
Cell Attention Networks	Sep 16, 2022	Graph AttentionGraph Classification	CodeCode Available	5
Embedding Graphs on Grassmann Manifold	May 30, 2022	Graph EmbeddingGraph Property Prediction	CodeCode Available	5
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events	Nov 11, 2020	Graph Representation LearningKnowledge Distillation	CodeCode Available	5

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Pi-net-linear	Error (mm)	0.47	—	Unverified