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 101–150 of 982 papers

TitleStatusHype
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on EchocardiogramsCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
How Powerful are Graph Neural Networks?Code1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Empowering Graph Representation Learning with Test-Time Graph TransformationCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
How Expressive are Transformers in Spectral Domain for Graphs?Code1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
LazyGNN: Large-Scale Graph Neural Networks via Lazy PropagationCode1
Expander Graph PropagationCode1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic GraphsCode1
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender SystemsCode1
Machine Learning on Graphs: A Model and Comprehensive TaxonomyCode1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
MAGNET: Multi-Label Text Classification using Attention-based Graph Neural NetworkCode1
Graph Contrastive Learning with Adaptive AugmentationCode1
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on GraphsCode1
Generative Subgraph Contrast for Self-Supervised Graph Representation LearningCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
GCondenser: Benchmarking Graph CondensationCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
CCGL: Contrastive Cascade Graph LearningCode1
A Representation Learning Framework for Property GraphsCode1
Information Obfuscation of Graph Neural NetworksCode1
Graph Autoencoder for Graph Compression and Representation LearningCode1
Certifiably Robust Graph Contrastive LearningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
Data Augmentation on Graphs: A Technical SurveyCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
Graph External Attention Enhanced TransformerCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
A step towards neural genome assemblyCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
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Benchmark Results

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