SOTAVerified

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

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Showing 51–100 of 982 papers

TitleStatusHype
Data Augmentation on Graphs: A Technical SurveyCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Graph Autoencoder for Graph Compression and Representation LearningCode1
Graph Barlow Twins: A self-supervised representation learning framework for graphsCode1
Certifiably Robust Graph Contrastive LearningCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learningCode1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on GraphsCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
A step towards neural genome assemblyCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Adversarial Graph DisentanglementCode1
Does Graph Distillation See Like Vision Dataset Counterpart?Code1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
A Representation Learning Framework for Property GraphsCode1
CCGL: Contrastive Cascade Graph LearningCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
A Gentle Introduction to Deep Learning for GraphsCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
Multi-hop Attention Graph Neural NetworkCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Deep Graph Contrastive Representation LearningCode1
A Large-Scale Database for Graph Representation LearningCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Disentangle-based Continual Graph Representation LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified